# Fast and scalable neuroevolution deep learning architecture search for
multivariate anomaly detection

Marcin Pietroń
Institute of Electronics
AGH University of Science and Technology
Cracow, Poland
<EMAIL_ADDRESS>
&Dominik Żurek
Institute of Computer Science
AGH University of Science and Technology
Cracow, Poland
<EMAIL_ADDRESS>
&Kamil Faber
Institute of Computer Science
AGH University of Science and Technology
Cracow, Poland
<EMAIL_ADDRESS>

###### Abstract

Neuroevolution is one of the methodologies that can be used for learning
optimal architecture during training. It uses evolutionary algorithms to
generate the topology of artificial neural networks (ANN) and its parameters.
The main benefits are that it is very scalable and can be fully or partially
non gradient method. The next point is that is not a rigid method and enables
easy modifications and extension with new algorithms inside it. In this work,
a modified neuroevolution technique is presented which incorporates multi-
level optimisation. The presented approach adapts evolution strategies for
evolving an ensemble model based on the bagging technique, using genetic
operators for optimising single anomaly detection models, reducing the
training dataset to speedup the search process and perform non-gradient fine
tuning. Multivariate anomaly detection as an unsupervised learning task is the
case study upon which the presented approach is tested. Single model
optimisation is based on mutation and crossover operators and is focused on
finding optimal window sizes, the number of layers, layer depths,
hyperparameters etc. to boost the anomaly detection scores of new and already
known models. The proposed framework and its protocol shows that it is
possible to find architecture within a reasonable time frame which can boost
all well known multivariate anomaly detection deep learning architectures. The
work concentrates on improvements to the first multi level neuroevolution
approach for anomaly detection [1]. The main modifications are in the methods
of mixing groups and single model evolution, non-gradient fine tuning and a
voting mechanism. The presented framework can be used as an efficient learning
network architecture method for any different unsupervised task where
autoencoder architectures can be used. The tests were run on SWAT and WADI
datasets and the presented approach evolved the architectures that achieved
the best scores among other deep learning models.

_K_ eywords neuroevolution, anomaly detection, ensemble model, CNN, NAS, deep
learning

##  1 Introduction

Neuroevolution is a form of artificial intelligence that uses evolutionary
approaches to find efficient neural networks. The most popular forms of
neuroevolution algorithms are NEAT [2], HyperNEAT [3], coDeepNEAT [4] other
solutions described in the literature [1]. The presented approach is an
enhancement and improvement of the work in [1]. The solution presented in [1]
is partially based on the coDeepNEAT algorithm which is used for learning deep
neural architectures. The approach presented in [1] consists of two
populations. The first is the model’s population from which new single models
are evolved by genetic operators. The second is the subgroup population, which
is needed to form the subsets of sensors. Finally the algorithm sets up the
ensemble model from optimised architectures based on the bagging technique.
The fitness during search is the sum of F1 scores from the training dataset
and from the random reduced validation dataset. This work presents
improvements for presented multi-level neuroevolution. The one novelty of the
proposed enhancements is that the subgroup models in each ensemble solution
can be evolved independently. The next extension is fast non gradient fine
tuning of the learnt network topology. In previous work the subgroup search
was run on initial base models to form optimal subgroups with subset of
sensors and then the evolution of the model was run. The sub-models in single
ensemble solution were all the same during the search process. The modified
protocol presented in this work allows the inclusion of different models in
each ensemble architecture. The case study upon which the presented approach
is tested is multivariate anomaly detection. The base deep learning models are
those which achieve the best results on well-known anomaly-detection
benchmarks. These are mainly autoencoders based on LSTM layers, convolutional
or fully connected sequences of layers. These can have a wide variety of
enhancements and form variational, denoise, adversarial autoencoders or by a
discriminator as an additional verification module be a GAN-based autoencoder.
Recently, we have also been able to observe promising results using deep graph
neural networks in anomaly detection [5]. The work shows that the described
improved multi-level neuroevolution search can learn deep ensemble
autoencoders that can outperform all existing solutions including graph neural
networks. The paper is organised as follows. The second section describes
related works. The third section presents the neuroevolution approach with the
main improvements. The next section concerns learnt models and results.
Finally, the conclusions and future works are formulated.

##  2 Related works

Recently neuroevolution algorithms have been used in many machine learning
tasks for improving the accuracy of deep learning models and finding new
network topologies [6]. In [7], the neuroevolution search is used for evolving
neural networks for object classification in high resolution remote sensing
images. In [8], the authors present a neuroevolution algorithm for standard
image classification. The authors in [4] show the neuroevolution strategy
scheme for language modeling, image classification and object detection tasks.
This strategy is based on the co-evolutionary deep NEAT algorithm which has
two levels of optimisation. The first level is single deep learning sub-block
optimisation. The second level is composition of sub-blocks to form a whole
network. The presented results showed that in most cases optimised models
achieved better results than models designed by humans. The first approach for
a co-evolutionary deep neuroevolution-based multivariate anomaly detection
system is presented in [1]. It present a two level learning network
architecture approach. The first level is single deep autoencoder evolution,
the second is the optimal sensor subgroup search to form an ensemble model.
The presented approach outperforms all non-graph-based deep learning models on
most popular benchmarks. The authors show that a multi-level neuroevolution
based approach can achieve better results than the best known deep
autoencoders like the LSTM-VAE model [9], the 1D convolution autoencoder [10]
and GAN-based USAD [11] and [12]. The graph-based neural network presented in
[5] with an f1-score of 81% for SWAT and 57% for WADI datasets was the only
model that outperformed the neuroevolution-based system. The described
enhancements improve the ensemble model beyond these results which makes the
learnt architecture the best all the anomaly detection models.

##  3 Neuroevolution ensemble approach

An ensemble model based on dividing available features into smaller groups and
training each model on a separate subset of features can guarantee that models
can discover more precise dependencies and relations between features. The
prototype of our framework presented in Figure 1 can build such an ensemble
model and consists of two separate populations. The first one is the models
population. The second is the data subgroup population. In previous work [1],
a single network was evolved for one ensemble model. This work describes the
protocol in which the models in each subgroup are modified independently.

A simplified schema of our approach is presented in Algorithm 1. The algorithm
starts by reducing the training data for the evolution process (line 1,
algorithm 1). The downsampling of data is described in eq.1 and eq. 2. One
sample is formed by calculating the mean average value of a sequence of
consecutive sample.

$S_{x}=\\{X\\_red_{t},t\in{1,2,...,N/red\\_ratio}\\}$ (1)

$X\\_red_{t}=\frac{\sum_{i=red\\_ratio}^{t-i}X_{t}}{red\\_ratio}$ (2)

$X_{t}=\\{x_{i},i\in\\{1,2,...,nr\\_of\\_sensors\\}\\}$ (3)

The single model is a sequence of encoder and decoder layers with possible
residual branches (eq. 4).

$F_{\Theta_{N}}^{i}(X)={f_{\theta_{N}}^{i}(f_{\theta_{N-1}}^{i}...(f_{\theta_{0}}^{i}(X)))}$
(4)

The ensemble model is defined as a set of single models (eq. 5) working on a
subgroup of input sensors (eq.6, eq.7). We apply a neuroevolution approach for
searching for the optimal partitioning into groups.

$F_{ensemble}^{i}(X)\iff(F_{\Theta}^{i0}(x_{i0}),F_{\Theta}^{i1}(x_{i1}),...,F_{\Theta}^{iM}(x_{iM}))$
(5)

$x_{i0}\land x_{i1}...\land x_{iM}\subset X$ (6)

$|x_{ij}|\leq nr\\_of\\_sensors,\forall j\in\\{1,2,..,M\\}$ (7)

Populations of subgroups and models are defined as in eq.9 and eq.8 :

$P_{E}=\\{F_{ensemble}^{i},i\in\\{1,2,...,N_{E}\\}\\}$ (8)

$P_{M}=\\{F_{\Theta_{N}}^{i},\Theta=\\{\theta_{0},\theta_{1},...,\theta_{N}\\}\\}$
(9)

The models in the subgroups are evolved in an independent way (eq.13, eq.11).
These models are mutated and crossed over on a subset of sensors (eq.10,
eq.12). Therefore, the final ensemble model is heterogenous and the ensemble
model consists of different models in each subgroup. The formalism for genetic
operators in the evolution of the subgroups is the same as in [1]. The
improvement in models evolution is that after crossover and mutation
operation, the distance function (mainly based on length of the model, eq.14)
is computed to diversify offspring population (not only best models are chosen
for the next iteration but also those which are different from each other to
avoid convergence to local optimum).

Figure 1: The architecture of the framework

$c:Parents\times layer\\_id\rightarrow Children$ (10)

$c(F_{\Theta_{M}}^{mj}(x_{mj}),F_{\Theta_{N}}^{nj}(x_{nj}),l\\_id)\rightarrow
F_{\Theta_{M^{\prime}}}^{mj}(x_{mj}),F_{\Theta_{N^{\prime}}}^{nj}(x_{nj})$
(11)

$m:Parent\times layer\\_id\times layer\\_feature\rightarrow Child$ (12)

$m(F_{\Theta_{N}}^{ij}(x_{ij}),l\\_id,l\\_feature)\rightarrow
F_{\Theta_{N^{\prime}}}^{ij}(x_{ij})$ (13)

$distance(F_{\Theta_{M}}^{ij}(x_{ij}),F_{\Theta_{N}}^{ij}(x_{ij}))=|\Theta_{M}|-|\Theta_{N}|$
(14)

Finally, when the optimised ensemble model is obtained, non-gradient fine
tuning is invoked. Fine tuning is performed as weight mutation on a pretrained
model (eq.15 eq.16). The additional input parameters for mutation are the
percentage of weights to be mutated in each layer and the scaling factor for
computing the maximum value by which the weights can be modified (eq.17).

$m:Parent\times percentage\times scale\rightarrow Child$ (15)

$F_{\Theta^{\prime}}^{i^{\prime}}=m(F_{\Theta}^{i}),\Theta^{\prime}=\\{\theta^{\prime}_{0},\theta^{\prime}_{1},...,\theta^{\prime}_{N}\\},\Theta^{i}\in\\{\mathbb{R}^{4},\mathbb{R}^{2}\\}$
(16)

$\theta^{\prime}_{i}=\theta_{i}\pm\alpha*\frac{max(abs(\theta_{i}))}{scale}$
(17)

The layer weights are modified using the mask (eq.19) which is set randomly
and is a binary tensor with the same size as the corresponding weight tensor.
The number of ’1’ values is equal to the ’percentage’ input parameter (eq.19,
algorithm 4).

$\theta_{mask}^{i}\in\\{\\{0,1\\}^{4},\\{0,1\\}^{2}\\}$ (18)

$\sum_{j=0}^{size}\theta_{mask}^{i}(j)=percentage$ (19)

After classifying data points using every model, we use a voting mechanism to
determine whether a data point should be considered as an anomaly by the whole
ensemble model (line 5, algorithm 1).

Result: Classification of the anomalies

1 Data reduction - downsampling;

2 Find the best partition of features into groups using a genetic algorithm;

3 Evolving a separate model for every subgroup;

4 Fine tune model without gradient;

Evaluate an ensemble model using a voting algorithm

Algorithm 1 Simplified general schema of our approach

To find the optimal partition of features into groups, the genetic algorithm
is applied. The single gene provides the information that a feature $f$ is
present in a group $t$. The single solution represents $k$ groups, each
containing zero or more features. A sample solution for $k=3$ could be:
$[[0,1,5,12],[2,3,4,9],[6,7,9]]$, where numbers in groups mean which features
are present. Population $P$ contains $N_{E}$ solutions (eq.8).

Result: Final population after $N_{g}$ generations

Input: $N_{E}$ \- Size of ensemble population

Input: $N_{M}$ \- Size of models population

Input: $N_{g_{E}}$ \- Number of generations in the genetic algorithm

Input: $N_{g_{M}}$ \- Number of generations in the genetic algorithm

Input: $k$ \- Max number of groups in a single solution

1 Generate initial population;

2 $generation=0$ ;

3 while  _$generation <N_{g_{E}}$_ do

4 For every solution in the population calculate the fitness;

5 Choose the best solutions as parents;

6 Create offspring using crossover;

7 Mutate offspring;

8 $generation\leftarrow generation+1$

9for  _$ensemble\\_model$ $\in$ range$(N_{E})$_ do

10 for  _$model$ $\in$ range$(size(ensemble\\_model))$_ do

11 Generate initial population;

12 while  _$generation <N_{g_{M}}$_ do

13 For every model in population $N_{M}$ calculate a fitness;

14 Choose the best solutions as a parents;

15 Create offspring using crossover;

16 Mutate offspring;

17 $generation\leftarrow generation+1$

18Fine tuning;

Return final population

Algorithm 2 Genetic algorithm flow

To improve convergence of a genetic algorithm, instead of using a random
initial population, we can create it based on the correlation between
features. We use a hierarchical clustering with the addition of a little
randomness to achieve a diverse population. The method for calculating fitness
for a single solution is presented in algorithm 3. For every used dataset
(SWAT and WADI), we split a normal part of the data into training and
validation datasets. We calculate fitness for every feature group in the
solution. As the first step, we train a chosen model on selected features from
the training data for a given number of epochs (line 3, algorithm 3). After
that, we evaluate the trained model on training and validation data (lines 4
and 5, algorithm 3) calculating losses. To normalise loss, we calculate the
weighted loss from the training and validation datasets (lines 6 and 7) and we
also divide the weighted loss by the number of features in the group (line 8,
algorithm 3). The final fitness for every solution is calculated as a negated
sum of losses for groups in the solution (lines 9 and 10, algorithm 3). The
value is negated because we want to minimise the total loss of an ensemble
model, while in the genetic algorithm, the goal is to maximise the fitness.
The same fitness calculation is used for sensor subsets and models evolution.

Result: Fitness value for solution $S$

Input: $S$ \- solution

Input: $X_{t}$ \- train dataset

Input: $X_{v}$ \- validation dataset

Input: $N_{ep}$ \- number of epochs to train while calculating fitness

1 $loss_{sum}=[]$ ;

2 for  _$g$ $\in$ $S$_ do

3 $model=\operatorname{train\\_model}{(X_{t},g,N_{ep})}$;

4 $loss_{t}=\operatorname{evaluate}{(model,X_{t},g)}$;

5 $loss_{v}=\operatorname{evaluate}{(model,X_{v},g)}$ ;

6 $N_{X}=\operatorname{len}{(X_{t})}+\operatorname{len}{(X_{v})}$ ;

7
$loss_{w}=\frac{\operatorname{len}{(X_{t})}}{N_{X}}*loss_{t}+\frac{\operatorname{len}{(X_{v})}}{N_{X}}*loss_{v}$;

8 $loss_{g}=\frac{loss_{w}}{\operatorname{len}{(g)}}$;

9 $loss_{sum}=loss_{sum}+loss_{g}$

$return-loss_{sum}$

Algorithm 3 Fitness calculation

In algorithm 2 the whole flow of the evolutionary process is described. From
line 3 to 8 the sensor subgroup evolution is run. The same mutation and
crossover operations are performed as in [1]. In lines 9 to 17, the
optimisation of single models in each ensemble model is computed. The last
step is fine tuning (line 18). The algorithm of fine tuning is presented in
algorithm 4. The main operation is mutation (line 3) in which a random
constant number of weights are perturbed.

Result: Fine tuned models $S$

Input: $N_{P}$ \- Size of pretrained ensemble population

Input: $N_{g}$ \- Number of generations in the fine tuning

Input: $percentage$ \- percentage of weights to be perturbated

Input: $scale$ \- scaling factor for magnitude perturbation

1 $generation=0$ ;

2 while  _$generation <N_{g}$_ do

3 Mutate randomly ’percentage’ of weights;

4 For every solution in the population calculate the fitness;

5 Choose the best solutions and include them in the new population;

6 $generation\leftarrow generation+1$

Return final population of fine tuned models

Algorithm 4 Fine tuning

All parameters for the neuroevolution approach in this work are:

  * •

$k$ \- maximal number of submodels in an ensemble model,

  * •

$p_{m}$ \- probability of the mutation in a single group of features,

  * •

$N_{g_{E}}$ \- number of generations in a genetic subgroup algorithm,

  * •

$N_{g_{M}}$ \- number of generations in a genetic model algorithm,

  * •

$N_{E}$ \- size of the population in a genetic subgroup algorithm,

  * •

$N_{M}$ \- size of the population in a genetic model algorithm,

  * •

$percentage$ \- percentage of weights,

  * •

$scale$ \- scale factor,

  * •

$N_{par}$ \- number of parents mating,

  * •

$N_{ep}$ \- number of epochs to train while calculating fitness.

Offspring created via a crossover algorithm can also be affected by mutations
(line 7, algorithm 2).

Table 1: Parameters of genetic algorithm for subgroup search Parameter | Basic value | Rerun Value
---|---|---
Population size | 8 | 16
Number of parents mating | 4 | 8
Mutation probability | 0.1 | 0.1
Number of generations | 10 | 10
Table 2: Parameters of genetic algorithm for model search Parameter | Basic value | Rerun Value
---|---|---
Population size | 24 | 48
Number of parents mating | 8 | 16
Mutation probability | 0.5 | 0.5
Number of generations | 16 | 16
Table 3: Parameters of fine tuning Parameter | Basic value | Rerun Value
---|---|---
Population size | 32 | 64
Mutation probability | 0.02 | 0.02
Number of generations | 64 | 64
Table 4: Statistics of the used datasets Datasets | #Features | #Train | #Test | #Anomalies
---|---|---|---|---
SWAT | 51 | 49668 | 44981 | 11.97%
WADI-2017 | 123 | 1048571 | 172801 | 5.99%
WADI-2019 | 123 | 784571 | 172801 | 5.77%
Table 5: Statistics of the used datasets in evolution Dataset |  | #Train
---
in evolution
| #Epoch
---
in evolution
| #Epoch
---
in final traning
SWAT | 9 933 | 25 | 90
WADI-2019 | 156 914 | 25 | 120

###  3.1 Models

The entire process of obtaining the most effective models has taken 20h for
the SWAT dataset and 62H for the WADI dataset. The evolution process was
incorporated with a downsampling process which calculated the average from
_k=5_ consecutive rows. Thanks to this it was possible to reduce evolution
time. Moreover the usage of the downsampling process has improved the result
by around 4% for the SWAT dataset and 5% for the WADI dataset. The tables 6
and 7 contain the parameters of the most effective architectures which were
explored by the presented neuroevolution approach, including searching for the
optimal window size, the number of layers, and the number of neurons in
layers. As it turns out in both cases the number of layers is three but the
number of neurons is higher in the case of the WADI dataset. In order to check
the final efficiency of those generated models, they are trained over 100
epochs in the case of WADI dataset and over 80 epochs in the case of SWAT
dataset and finally the test phase is run on the test dataset. Moreover, the
fine tuning based on weights mutation is performed as the last stage of the
optimisation (see section 3).

Table 6: Hyperparameters of the most effective CNN 1D model for the SWAT dataset. | Layer | Parameters |  | Layer | Parameters
---|---|---|---|---|---
1 | Conv1D |  | in channels = 5
---
out channels = 84
kernel size = 2
padding = 1
7 | Conv1D |  | in channels = 205
---
out channels = 123
kernel size = 4
padding = 1
2 |  | Batch
---
Norm1D
number of features = 84 | 8 |  | Batch
---
Norm1D
number of features = 123
3 | Conv1D |  | in channels = 84
---
out channels = 123
kernel size = 6
padding = 1
9 | Conv1D |  | in channels = 123
---
out channels = 84
kernel size = 6
padding = 1
4 |  | Batch
---
Norm1D
number of features = 123 | 10 |  | Batch
---
Norm1D
number of features = 84
5 | Conv1D |  | in channels = 123
---
out channels = 205
kernel size = 4
padding = 1
11 | Conv1D |  | in channels = 84
---
out channels = 5
kernel size = 2
padding = 1
6 |  | Batch
---
Norm1D
number of features = 205 | 12 |  | Batch
---
Norm1D
number of features = 5
Table 7: Hyperparameters of the most effective CNN 1D model for the WADI dataset. | Layer | Parameters |  | Layer | Parameters
---|---|---|---|---|---
1 | Conv1D |  | in channels = 6
---
out channels = 91
kernel size = 7
padding = 1
7 | Conv1D |  | in channels = 155
---
out channels = 153
kernel size = 4
padding = 1
2 |  | Batch
---
Norm1D
number of features = 91 | 8 |  | Batch
---
Norm1D
number of features = 153
3 | Conv1D |  | in channels = 91
---
out channels = 153
kernel size = 3
padding = 1
9 | Conv1D |  | in channels = 153
---
out channels = 91
kernel size = 3
padding = 1
4 |  | Batch
---
Norm1D
number of features = 153 | 10 |  | Batch
---
Norm1D
number of features = 91
5 | Conv1D |  | in channels = 153
---
out channels = 155
kernel size = 4
padding = 1
11 | Conv1D |  | in channels = 91
---
out channels = 6
kernel size = 7
padding = 1
6 |  | Batch
---
Norm1D
number of features = 155 | 12 |  | Batch
---
Norm1D
number of features = 6

##  4 Experiments and results

In this section the improvements that were possible to achieve through the
usage of proposed solution are demonstrated. We provide a comparison with
state-of-the-art works. All of our presented calculations were performed on
the Nvidia Tesla V100-SXM2-32GB111https://www.nvidia.com/en-us/data-
center/v100/. In order to reduce both training times, i.e during the evolution
algorithm and the final training, each subgroup/model is processed on a
separate GPGPU. The values of the parameters of the genetic algorithm of the
subgroup search are presented in Table 1 (column basic value). Similar to the
evolution of the subgroup the models search was performed with the parameters
presented in the table 2. Table 3 contains the parameters of the genetic
algorithms which realise fine tuning based on the weights mutation of the most
effective models. These parameters are the same for both used datasets. During
this process the weights could be changed by about +/- $\frac{1}{256}$ of its
maximum value where ’+’ or ’-’ is randomly chosen. The number of weights which
can be increased/decreased in a single mutation operation is determined by the
mutation probability parameter (see 3) and in our case, it equals 2%. Table 9
presents the result after splitting the datasets into subgroups, performing
the model evolution and finally applying fine tuning by weight mutation. The
impact of splitting into the groups through the usage of the genetic
algorithms itself, caused two-times improvement on the WADI dataset and 1% on
SWAT dataset (’Basic CNN 1D’ in Table 9). Through the usage of genetic
algorithms for single models in subgroups it was possible to improve results
by about 3% for both datasets (’single CNN 1D subgroup evolution’ in Table 9).
Use of the non-gradient fine tuning on the evolved models improved the result
by about 3% in the case of the WADI dataset and about 2% in the case of the
SWAT dataset (’single CNN 1D fine tuned’ in Table 9).

Table 8: Anomaly detection accuracy (precision (%), recall(%), F1-score(%)) on two datasets without splinting into groups. Results marked as * was generated by usage of the WADI-2019 dataset. ** means that we had to reimplement a model on our own [1] | SWAT | WADI
---|---|---
Method | Prec | Rec | F1 | Prec | Rec | F1
MAD-GAN | 98.97 | 63.74 | 0.77 | 41.44 | 33.92 | 0.37
USAD | 98.51 | 66.18 | 0.79 | 99.47 | 13.18 | 0.23
USAD** | 88.21 | 65.29 | 0.75 | 26.28* | 35.31* | 0.30*
CNN 1D | 94.25 | 67.92 | 0.78 | 39.30* | 20.28* | 0.27*
GDN | 99.35 | 68.12 | 0.81 | 97.50 | 40.19 | 0.57
LSTM-VAE | 95.69 | 55.18 | 0.72 | 21.22 | 29.12 | 0.28
USAD | 98.10 | 66.01 | 0.79 | 71.24 | 31.41 | 0.43
CNN 1D | 95.24 | 63.73 | 0.78 | 63.76 | 43.54 | 0.27
Table 9: Anomaly detection accuracy (precision (%), recall(%), F1-score(%)) on two datasets after splitting into groups, perform mutation and fined tuned | SWAT | WADI*
---|---|---
Method | Prec | Rec | F1 | Prec | Rec | F1
Basic CNN 1D [1] | 93.61 | 69.40 | 0.79 | 67.25 | 52.18 | 0.54
single CNN 1D subgroup evolution | 93.61 | 69.40 | 0.80 | 67.25 | 52.37 | 0.59
single CNN 1D fine tuned | 97.77 | 70.48 | 0.82 | 77.03 | 51.64 | 0.62

##  5 Conclusions and future work

This paper has presented a method for evolving ensemble deep learning
autoencoders architectures for anomaly detection. The achieved results show
that the presented approach is very efficient and all steps of the algorithm
improve the accuracy of anomaly detection. They guarantee to outperform all
known solutions in the field of multivariate time series anomaly detection.
The main contribution of this paper is the design of the new neuroevolution-
based learning network architecture framework. The most important feature is
its multi-level optimisation. It optimizes not only the topology of the models
but also reduces training data and forms an ensemble model with non-gradient
fine tuning mode. Future work will concentrate on further enhancements of the
algorithm. The framework will be incorporated with redefined genetic operators
for graph neural networks and modified crossover for mixing different
architectures together (e.g. convolutional with LSTM in one single ensemble
model),adding a discriminator with its loss. The next point which should be
further explored is training data distillation to reduce the time of the
learning optimal network architecture. The next goal is to add incremental
semi-supervised learning in the testing phase.

## Acknowledgment

This research was supported in part by PLGrid Infrastructure.

## APPENDIX

###  5.1 Algorithms

formalism and all algorithms

###  5.2 Complexity analysis

execution times

###  5.3 Ablation study

impact of parameters to results

###  5.4 Framework architecture

code description, diagrams and figures

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# Transparency in Multi-Human Multi-Robot Interaction

Jayam Patel1*  —, Tyagaraja Ramaswamy1, Zhi Li1 and Carlo Pinciroli1*  — 1
Department of Robotics Engineering, Worcester Polytechnic Institute, MA, USA.
Email: {jupatel, tramaswamy, zli11<EMAIL_ADDRESS>
###### Abstract

Transparency is a key factor in the performance of human-robot interaction. A
transparent interface allows operators to be aware of the state of a robot and
to assess the progress of the tasks at hand. When multi-robot systems are
involved, transparency is a greater challenge, due to the larger number of
variables affecting the behavior of the robots as a whole. Existing work
studies transparency with single operators and multiple robots. Studies on
transparency that focus on multiple operators interacting with a multi-robot
systems are limited. This paper fills this gap by presenting a novel human-
swarm interface for multiple operators. Through this interface, we study which
graphical elements are contributing to multi-operator transparency by
comparing four “transparency modes”:  (i) no transparency (no operator
receives information from the robots),  (ii) central transparency (the
operators receive information only relevant to their personal task),  (iii)
peripheral transparency (the operators share information on each others’
tasks), and  (iv) mixed transparency (both central and peripheral).  We report
the results in terms of awareness, trust, and workload from a user study
involving 18 participants engaged in a complex multi-robot task.

##  I Introduction

Human-robot teams are often envisioned in complex scenarios [1], including
humanitarian missions [2, 3], interplanetary exploration [4], ecosystem
restoration [5, 6], mining [7], bridge inspection [8] and surgery [9]. The
success of these missions depends on effective and efficient team interaction.
One crucial requirement to make this vision a reality is making the multi-
robot system more transparent [10], i.e., legible and interpretable, for the
human operators.

Transparency is a key property of any human-machine interface. Transparent
interfaces offer high usability and foster increased situational awareness
[10, 11, 12, 13, 14]. Transparent interfaces limit or remove ambiguity,
improve trust, and enhance decision-making [15, 16]. Lyon’s models of
transparency [17] and the situational awareness-based transparency (SAT) [18]
model provide guidelines for an effective interaction between an operator and
a machine.

However, these models are designed and tested with a single operator in mind.
The problem of designing a transparent interface intensifies when there are
multiple human operators, or ‘the machine’ is, in fact, a multi-robot system.
This is because the heterogeneous nature and sheer number of combined
interactions among operators and robots affect the behavior and the
performance of the entire system in non-trivial ways. As an example, imagine
an automated warehouse in which hundreds of robots navigate and transport
heavy objects. The robots might drop objects, experience hardware failure, or
transport an incorrect object. To resolve these issues, the operators must
collaborate and resolve the issues using information from robots and other
operators.

In this paper, we study the information that each human operator must process
and use, which affects cognitive load [19, 20]. To decrease cognitive load, a
possible approach is to limit the amount and the type of information presented
to the operator. However, this creates a trade-off with transparency, which
intuitively suggests more information would be better.

We explore the design space of graphical user interfaces for human-robot
interaction, focusing on the multi-human multi-robot scenario, which has, so
far, received limited attention. We consider four types of interfaces, each
presenting different amounts and types of information, and each corresponding
to a specific ‘transparency mode’.

Figure 1: Central and peripheral regions of the field of view. The green
region indicates the central field of view. The yellow region indicates the
peripheral field of view.

To characterize these modes, we study the effect of the field of view (FoV) an
interface offers to the operators. We define the FoV as the observable area an
operator can see through the interface camera. As shown in Fig. 1, we
categorize the FoV into two regions, based on the distance from the center of
the screen: central and peripheral. The central FoV is the region closest to
the center; the peripheral FoV is the remaining region. Using this
categorization, we produced four types of interfaces, each differing in the
way information is displayed:

  * •

No Transparency (NT): no information is available to the operator, used as a
baseline for comparison;

  * •

Central Transparency (CT): information is available at the center of the FoV
and displayed directly on the robots;

  * •

Peripheral Transparency (PT): information is shown at the boundaries of the
FoV as dedicated widgets;

  * •

Mixed Transparency (MT): a combination of central and peripheral transparency.

We investigate the effects of the transparency modes on operator performance,
awareness, task load, and trust in the system. This paper offers two main
contributions:

  1. 1.

A novel augmented-reality-based interface for multi-human multi-robot
interaction with the mentioned transparency modes. This interface is an
improvement of our mixed-granularity control interface [21, 22] for single
operators;

  2. 2.

A study, which to the best of our knowledge is the first, of the effects of
transparency in multi-human multi-robot interaction. Our user study involved
18 participants in teams of 2, each team controlling 9 robots in an object
transport scenario.

The paper is organized as follows. In Sec. II, we discuss related work on
transparency. In Sec. III, we present our system and its design. In Sec. IV,
we report our user study procedures and results followed by analysis in Sec. V
and summarize the paper in Sec. VI.

##  II Background

Transparency is an important research topic in human-machine and human-robot
interaction [23, 24]. Transparency affects usability [25, 26, 27], performance
[28, 29], trust [30, 31] and explainability [32, 33]. The effect of these
factors increases with the type and quantity of information provided to the
operator [34, 35]. Coarse information often negatively affects decision time,
trust, situational awareness, and performance; in contrast, detailed
information typically results in higher cognitive load.

Ghiringhelli et al. [36] first proposed to graphically represent the actions
of the robots using augmented reality for single operators. Chen et al. [13]
and Mercado et al. [37] tested the impact of transparency on situational
awareness, trust, and workload of an operator. Their work is based on
simulated point-mass models of the robots, which lack important physical
properties of mobile robots and create a ‘reality gap’ between results
collected with a simulated environment and the results collected with physical
environment [38].

With multiple operators, a novel problem arises: the need for operators to
share robots and their information, to achieve a new form of transparency
which we call operator-level transparency. To the best of our knowledge, there
is no study on this topic in the context of multi-robot systems controlled
through augmented reality (AR).

##  III Transparency-based Interaction System

###  III-A System Overview

Our system comprises four components (see Fig. 2):

  1. 1.

A distributed AR interface implemented as an app for an Apple iPad;

  2. 2.

A team of robots, pre-programmed with various behaviors to reach a defined
point, recognize objects, and perform collective transport;

  3. 3.

Vicon [39], a motion capture system that localizes the robots and the movable
objects in the environment;

  4. 4.

ARGoS [40], a multi-robot simulator modified to act as ‘software glue’ between
the app, the robots, and the Vicon. We replaced the simulated physics engine
shipped with ARGoS with a plug-in that receives positional data from the Vicon
motion capture system, and developed new sensor and actuator plug-ins that
interface with those on-board the robots. With these plug-ins, ARGoS acts as a
middleware functionally similar to the roscore of ROS.

Figure 2: System overview.

###  III-B User Interface

Our interface integrates an AR software development kit, Vuforia [41], and the
Unity [42] game engine. The interface detects robots and movable objects by
their fiducial markers. The robots and objects recognized by the interface are
overlaid by virtual objects. The operator can manipulate the virtual objects
to send commands to the robots. For example, the operator can translate a
virtual object with a one-finger swipe and rotate it with a two-finger twist.
It is also possible to select a team of robots by drawing a closed path with a
continuous one-finger swipe. Fig. 3 shows a screenshot of the default view of
the application. The top-right corner shows the menu buttons to toggle the
visibility of the transparency modes. The bottom-left corner shows the real-
time global coordinate frame.

Figure 3: Screenshot of our augmented-reality interface running on an iPad.
The black arrow indicates the origin marker that corresponds to the coordinate
frame of the interface.

###  III-C Granularity of Control

In our previous work [21, 22], we proposed an interface capable of mixed
granularity of control for single operators. The ‘granularity’ refers to the
possibility to interact at the robot-, team-, and environment-level. Robot-
and team-level control allow the operator to send direct commands to
individual robots or groups of them. With environment-level granularity, the
operator indicates the desired effect of a modification of the environment,
e.g., moving an object, and the robots autonomously execute the action. As
discussed in [21, 22], mixed granularity of control can outperform any
individual level of control. We use these interface features in the present
study.

(a) Object recognition

(b) New Goal Defined

(c) Robots approach and push

(d) Transport complete

Figure 4: Object manipulation by interaction with virtual objects. The
overlaid dotted black arrow indicates the one-finger swipe gesture used to
move the virtual object and the overlaid red dotted arrow indicates the two-
finger rotation gesture.

Object Manipulation. The interface overlays virtual objects over the
recognized objects (see Fig. 4). The user can move multiple virtual objects to
define their respective desired poses, and teams of robots transport these
objects to destination. If two or more operators simultaneously control the
same object, the system processes the pose received last.

(a) Robot recognition

(b) New robot position

Figure 5: Robot manipulation by interaction with virtual robots. The overlaid
dotted black arrow indicates the one-finger swipe gesture to move the virtual
robot and the arrowhead color indicates the moved virtual robots.

Robot Manipulation. The interface overlays virtual robots over the recognized
robots (see Fig. 5). The color of the virtual robot resembles the color of the
fiducial markers to differentiate between robots. The user can move multiple
virtual robots to define their respective desired poses. If the robot is part
of a team performing collective transport, the other robots in the same team
pause until the selected robot reaches the desired pose. If the robot is part
of a team not involved in collective transport, the selected robot overwrites
the goal pose with the newly defined pose and does not affect its team
members. If two or more operators simultaneously control the same robot, the
system processes the last pose received. Fig. 5 shows how virtual robots look.

(a) Robot team selection

(b) Robot team creation

(c) Robot team manipulation

(d) Robot team re-positioned

Figure 6: Robot team creation and manipulation by interacting with the
interface. The overlaid dotted black arrow indicates the one-finger swipe
gesture to move the virtual cube for re-positioning the the team of robots.

Robot Team Selection and Manipulation. The user can draw a closed path with a
one-finger continuous swipe to select all the robots in the enclosed region
(see Fig. 6). A contour-shaped virtual object with a virtual cube at its
centroid appears in the graphical view. The user can manipulate this cube to
define the desired pose for the selected team of robots. The user can handle
only one team at a time. If two or more operators have the same robot in their
team, the robot processes the pose received last.

###  III-D Collective Transport

Figure 7: Collective transport state machine.

We employ a collective transport behavior based on the finite state machine
(FSM) shown in Fig. 7. This behavior is identical to the one presented in our
previous work [21]. The states in the FSM are explained next.

Reach Object. Upon receiving the desired goal pose for the object, the robots
organize themselves around the object in a circular manner. These poses are
decided based on the number of robots in the team and their distance from the
object. This state comes to an end when all the robots reach their designated
poses.

Approach Object. The robots move towards the centroid of the object. This
state is completed when all the robots are in contact with the object.

Push Object. The robots first rotate in-place facing the direction of the
goal. The robots then move towards the goal. The robots modulate their speeds
to maintain a set distance from the centroid of the object and keep their
formation. If a robot breaks the formation, the team switches back to Approach
Object, waits for its completion, and subsequently resumes the transport
behavior. The state comes to an end once the object reaches the goal position.

Rotate Object. The robots rearrange around the object and move along a
circular path, thereby rotating the object in place. If any robot breaks the
formation, the team rearranges and resumes object rotation. The state ends
when the object reaches the desired orientation.

###  III-E Transparency Modes

We present different transparency modes based on the visual FoV of our
interface. The interface provides an option to switch between modes. The modes
incorporate transparency features that reflect an operator’s perception,
comprehension, and projection, i.e., in terms of the three levels described in
the SAT model [18]. Table I lists the features, the transparency modes, and
the corresponding the SAT levels of information.

TABLE I: Analogy between the features in our interface and the levels of the SAT model. Transparency mode | Our feature | SAT level
---|---|---
Central Transparency | On-robot status | Level 1 + 2
Robot Direction Pointer | Level 2
Shared Awareness | Level 3
Peripheral Transparency | On-robot status | Level 1 + 2
Object Panel | Level 2
Text-based Log | Level 3

No Transparency (NT). Operators can send control commands, but without access
to any feedback information.

Figure 8: Central Transparency showing on-robot status and directional
indicator.

Central Transparency (CT). The interface overlays each robot with a direction
vector and text to report the current task (see Fig. 8). The direction vector
indicates the heading of the robot. The color of the vectors resemble the
color of the fiducial markers to differentiate between vectors when the robots
are close to each other. The interface updates the information 10 times per
second. The displayed states are: Idle, Reach, and Error. The interface also
reports the commands of other operators in real time, to foster collaboration
and shared awareness, and to minimize (ideally avoid) conflicting control of
the same robots and objects. This information is only visible if an operator
is focusing the tablet camera on a specific robot or object, i.e., at the
center of the FoV.

Figure 9: Peripheral Transparency mode showing text-based lock, object panel
and robot panel (clockwise from top-left).

Peripheral Transparency (PT). The interface displays a robot panel, an object
panel, and a text-based log at the edges of the screen (see Fig. 9). The robot
panel shows the robots as icons. The highlighted icons correspond to the
robots that are moving or performing operator-defined actions. The interface
conveys error conditions as blinking red exclamation points. Analogously, the
object panel shows the objects as icons. The interface highlights the icons
that correspond to an object manipulated by the robots. The interface also
offers the option to select an object icon to lock it for future use. By
locking, an operator indicates that they intend to work with that object. The
interface of other operators highlights the lock with a red icon. An operator
can lock only one object at a time, removing past locks when a new one is
requested. The text-based log reports the last 3 control actions taken by
other operators.

Mixed Transparency (MT). This mode offers the features of both central and
peripheral transparency.

##  IV User Study

###  IV-A Hypotheses

The primary purpose of this work is to investigate the effect of different
transparency modes on the operators’ awareness, workload, trust, interaction,
and performance in a multi-human multi-robot scenario. We based our
experiments on three hypotheses:

  1. H1:

Mixed transparency (MT) has the best outcome as compared to other modes, in
terms of the mentioned metrics.

  2. H2:

Operators prefer mixed transparency (MT) to the other modes.

  3. H3:

Operators prefer central transparency (CT) to peripheral transparency (PT).

###  IV-B Gamified User Study

Figure 10: User study experiment setup. The shaded red box indicates the goal
region.

We devised a gamified scenario in which the operators must use the robots to
perform object transport. Teams of two participants had to move 6 objects (2
big and 4 small) from their initial position to a goal region. Big objects
were worth 2 points each, and small objects were worth 1 point each. The
operators had to work collaboratively to gain as many points as possible (out
of a maximum of 8) in a fixed time limit of 8 minutes. The operators could
move the big objects using the collective transport behavior, or using the
robot or robot-team manipulation modalities at will. Small objects could only
be transported with the robot and team control modalities. The operators were
given 9 robots to complete the game. Fig. 10 shows the initial positions of
the robots, the objects, and the goal region.

###  IV-C Participant Sample

We recruited 18 university students (10 female, 8 male) with ages ranging from
19 to 41 ($23.78\pm 5.08$) in accordance with protocols approved by WPI’s
IRB111https://www.wpi.edu/research/resources/compliance/institutional-review-
board. No participant had any prior experience with the system.

###  IV-D Procedures

Each session of the study approximately took 105 minutes and involved four
games. After signing the consent form, we explained the scenario and gave the
participants 10 minutes to play with the system. After each game, the
participants had to answer a subjective questionnaire. All the participants
played the game with all transparency modes (NT, CT, PT, MT) once. We
randomized the order of the modes to reduce learning effects.

###  IV-E Metrics

We recorded subjective and objective measures for each participant for each
task. We used the following measures:

Situational Awareness. We used the Situational Awareness Rating Technique
(SART) [43] on a 10-point Likert scale [44] to assess the situational
awareness after each game.

Task Workload. We used the NASA TLX [45] scale on a 4-point Likert scale to
compare the perceived workload in each game.

Trust. We used the trust questionnaire [46] on a 10-point Likert scale to
compare the trust in the interface affected by each transparency mode.

Interaction. We used a custom questionnaire (see Fig. 11) on a 5-point Likert
scale to assess the operator-level and robot-level interaction.

  * •

Did you understand your teammate’s intentions? Were you able to understand why
your teammate took a certain action?

  * •

Could you understand your teammate’s actions? Could you understand what your
teammate was doing at any particular time?

  * •

Could you follow the progress of the game? Were you able to gauge how much of
it was pending?

  * •

Did you understand what the robots were doing, i.e., how and why the robots
were behaving the way they did?

  * •

Was the information provided by the interface clear to understand?

Figure 11: Questionnaire to assess the operator-level and robot-level
interaction. Each question is expressed with a 5-point Likert scale.

Performance. We used the points earned in each game as a metric to scale the
performance achieved with each transparency mode.

Usability. We asked the participants to select the features (Log, Robot Panel,
Object Panel, and On-Robot Status) they used during the study. Additionally,
we asked them to rank the transparency modes from 1 to 4, 1 being the highest
rank.

TABLE II: Results with relationships between transparency modes. The relationship are based on mean ranks obtained through Friedman’s Test. The symbol ∗ denotes significant difference ($p<0.05$) and the symbol ∗∗ denotes marginally significant difference ($p<0.10$). The symbol - denotes negative scales and lower ranking is a good ranking. Attributes | Relationship | $\chi^{2}(3)$ |  $p$-value
---|---|---|---
SART SUBJECTIVE SCALE
Instability of Situation- | not significant | $4.192$ | $0.241$
Complexity of Situation- |  NT$>$MT$>$PT$>$CT∗∗ | $6.435$ | $0.092$
Variability of Situation- | not significant | $4.192$ | $0.241$
Arousal |  NT$>$MT$>$PT$>$CT∗∗ | $7.093$ | $0.069$
Concentration of Attention | not significant | $4.664$ | $0.198$
Spare Mental Capacity | not significant | $3.526$ | $0.317$
Information Quantity |  MT$>$CT$=$PT$>$NT∗ | $16.160$ | $0.001$
Information Quality |  MT$>$CT$>$PT$>$NT∗ | $11.351$ | $0.010$
Familiarity with Situation | not significant | $1.911$ | $0.591$
NASA TLX SUBJECTIVE SCALE
Mental Demand- | not significant | $6.169$ | $0.104$
Physical Demand- | not significant | $3.526$ | $0.317$
Temporal Demand- | not significant | $0.564$ | $0.903$
Performance- | not significant | $4.573$ | $0.206$
Effort- |  NT$>$PT$>$CT$>$MT∗ | $9.203$ | $0.027$
Frustration- |  NT$>$CT$>$MT$>$PT∗ | $9.205$ | $0.027$
TRUST SUBJECTIVE SCALE
Competence | not significant | $3.703$ | $0.295$
Predictability |  PT$>$CT$>$MT$>$NT∗∗ | $6.359$ | $0.095$
Reliability | not significant | $4.338$ | $0.227$
Faith | not significant | $1.891$ | $0.595$
Overall Trust |  PT$>$MT$>$CT$=$NT∗ | $12.607$ | $0.005$
Accuracy |  PT$>$MT$=$CT$>$NT∗ | $12.214$ | $0.007$
INTERACTION SUBJECTIVE SCALE
Teammate’s Intent |  MT$>$PT$>$CT$>$NT∗ | $23.976$ | $0.000$
Teammate’s Action |  MT$>$PT$=$CT$>$NT∗ | $22.511$ | $0.000$
Task Progress |  MT$>$CT$>$PT$>$NT∗ | $25.619$ | $0.000$
Robot Status |  CT$>$PT$>$MT$>$NT∗ | $13.608$ | $0.003$
Information Clarity |  CT$>$PT$>$MT$>$NT∗ | $12.078$ | $0.007$
PERFORMANCE OBJECTIVE SCALE
Points Scored | not significant | $5.554$ | $0.135$
Figure 12: Feature Usability. Figure 13: Game Preference. TABLE III: Ranking scores based on the Borda count. The gray cells indicate the leading scenario for each type of ranking. Borda Count | NT | CT | PT | MT
---|---|---|---|---
Based on Collected Data Ranking (Table II) | 17.5 | 40 | 39 | 43.5
Based on Preference Data Ranking (Fig. 13) | 18 | 46 | 45 | 72

##  V Analysis and Discussion

Table II shows the results for all the subjective scales and the objective
metrics. We used the Friedman test [47] to analyze the data and to assess the
significance among different games. We formed rankings based on the mean ranks
for all the attributes that showed statistical significance (set to $p<0.05$)
or marginal significance (set to $p<0.10$). Fig. 12 shows the percentage of
operators using a particular feature. Fig. 13 reports how participants ranked
the transparency modes.

We used the Borda count [48] to calculate the rankings. We inverted the
ranking of the negative scales when calculating the Borda count scores. Table
III shows the results for each category. This table indicates mixed
transparency (MT) as the overall winner in terms of performance, as well as
the preferred mode across participants, in accordance with hypotheses H1 and
H2. The data suggests that central transparency is better than peripheral
transparency, confirming hypothesis H3.

Mixed Transparency. This mode is the overall best choice for operators. The
data suggests that this mode has the best information quality and quantity.
The operators could pick the information they wanted from central and
peripheral regions. However, the operators reported higher perceived
complexity and higher arousal than with other transparency modes. The
usability tests suggested this mode as the best to understand the teammate’s
intent and actions. This justifies mixed transparency as the first choice.

Central Transparency. This mode has the lowest complexity and arousal. The
users found it easier to focus on the center of the screen, and use on-robot
status over the side panels. The users reported better information quality and
clarity w.r.t to peripheral transparency. $55.55\%$ of the operators preferred
this mode over peripheral transparency.

Peripheral Transparency. The operators found the information displayed at
periphery of the screen hard to parse and access. This led to increased
effort, complexity, and arousal w.r.t. the central transparency mode. However,
as the information was available on-demand and was not constantly displayed in
the FoV, the users reported the lowest amount of frustration. Operators also
preferred the icon panels over the text-based log. Additionally, the operators
preferred PT over CT to gain awareness of their teammate’s intention.

Performance. Our experiments did not report a substantial difference in
performance across transparency modes. We hypothesize that this lack of
difference is due to a learning effect across the four runs that each team had
to perform. We could not avoid this learning effect through randomization of
the transparency modes or pre-study training. The training sessions improved
the participants’ understanding of the interface and its features, but did not
improve operation proficiency. We attribute this issue to the fact that our
study was conducted with real robots, exposing the participants to real-world
issues with robots they never encountered before (e.g. noise, failures).

Fig. 14 reports the performance recorded in each game. Fig. 15 shows the
increase in performance sorted by game performed (learning effect). Task
performance dropped or stayed the same for teams that used no transparency
after using other transparency modes.

Figure 14: Task performance for each transparency mode. Figure 15: Learning
effect in the user study.

##  VI Conclusion and Future Work

In this paper, we studied the effects of different transparency modes in
multi-human multi-robot interaction. We classified transparency based on
visual FoV. We demonstrated the design of a novel augmented-reality interface
that supports different modes of transparency and provides both operator-level
and robot-level information.

We performed a user study with 18 operators to assess the effects of these
modes of transparency on awareness, workload, trust, and interaction. Mixed
transparency outperformed other modes in terms of overall effect and
usability, and the participants chose mixed transparency as the best mode. We
also compared central transparency with peripheral transparency. More
operators preferred central transparency (55.55%) over peripheral transparency
(45.45%). Although the difference between the central and peripheral
transparency is small, these modes of transparency have their respective
benefits. Central transparency offers better robot-level information, while
peripheral transparency provides better operator-level information.

We recognize that the sample size of our study is limited, making our study in
some ways exploratory from a statistical standpoint. However, the complexity
of the task we studied is compelling, especially when compared with existing
literature. The next iteration of our work will focus on expanding the user
study in two directions. First, understanding the effects of learning and
training on transparency, i.e., comparing the need of information for a novice
user with the needs of an expert user. Second, studying the effects of our
transparency features on the operator’s reaction time, i.e., the time taken to
resolve a problem.

## Acknowledgements

This work was funded by an Amazon Research Award.

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# Production of new superheavy Z=108-114 nuclei with 238U, 244Pu and 248,250Cm
targets

Zhao-Qing Feng<EMAIL_ADDRESS>Gen-Ming Jin  Jun-Qing Li  Institute of
Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, People’s Republic
of China

###### Abstract

Within the framework of the dinuclear system (DNS) model, production cross
sections of new superheavy nuclei with charged numbers Z=108-114 are analyzed
systematically. Possible combinations based on the actinide nuclides 238U,
244Pu and 248,250Cm with the optimal excitation energies and evaporation
channels are pointed out to synthesize new isotopes which lie between the
nuclides produced in the cold fusion and the 48Ca induced fusion reactions
experimentally, which are feasible to be constructed experimentally. It is
found that the production cross sections of superheavy nuclei decrease
drastically with the charged numbers of compound nuclei. Larger mass
asymmetries of the entrance channels enhance the cross sections in 2n-5n
channels.

PACS number(s)

25.70.Jj, 24.10.-i, 25.60.Pj

The synthesis of superheavy nuclei (SHN) is motivated with respect to search
the ”island of stability” which is predicted theoretically, and has obtained
much experimental research with fusion-evaporation reactions Ho00 ; Og07 .
Neutron-deficient SHN with charged numbers Z=107-112 were synthesized in cold
fusion reactions with the 208Pb and 209Bi targets for the first time and
investigated at GSI (Darmstadt, Germany) with the heavy-ion accelerator UNILAC
and the SHIP separator Ho00 ; Mu99 . Recently, experiments on the synthesis of
element 113 in the 70Zn+209Bi reaction have been performed successfully at
RIKEN (Tokyo, Japan) Mo04 . More neutron-rich SHN with Z=113-116, 118 were
assigned at FLNR in Dubna (Russia) with the double magic nucleus 48Ca
bombarding the actinide nuclei Og07 ; Og04 ; Og06 . New heavy isotopes 259Db
and 265Bh were also synthesized at HIRFL in Lanzhou (China) Ga01 . New SHN
between the isotopes of the cold fusion and the 48Ca induced reactions are of
importance not only for investigating the structure of SHN such as influence
of shell effect on stability of SHN etc, and also as a stepstone for further
synthesizing and identifying heavier superheavy nuclei.

Here we use a dinuclear system (DNS) model Fe06 ; Fe07 , in which the nucleon
transfer is coupled to the relative motion by solving a set of microscopically
derived master equations by distinguishing protons and neutrons, and a barrier
distribution in the capture and fusion process of two colliding nuclei is
introduced in the model. In the DNS model, the evaporation residue cross
section is expressed as a sum over partial waves with angular momentum $J$ at
centre-of-mass energy $E_{c.m.}$ Fe07 ; Fe09 ,

$\displaystyle\sigma_{ER}(E_{c.m.})=$ $\displaystyle\frac{\pi\hbar^{2}}{2\mu
E_{c.m.}}\sum_{J=0}^{J_{max}}(2J+1)T(E_{c.m.},J)$ (1) $\displaystyle\times
P_{CN}(E_{c.m.},J)W_{sur}(E_{c.m.},J).$

Here, $T(E_{c.m.},J)$ is the transmission probability of the two colliding
nuclei overcoming the Coulomb barrier in the entrance channel to form the DNS.
The $P_{CN}$ is the probability that the system will evolve from a touching
configuration to the formation of compound nucleus in competition with the
quasi-fission of the DNS and the fission of heavy fragment. The last term is
the survival probability of the formed compound nucleus, which can be
estimated with the statistical evaporation model by considering the
competition between neutron evaporation and fission Fe06 .

Within the concept of the DNS, the fusion probability was also calculated by
using the multidimensional Kramers-type expression to get the fusion and
quasifission rate by Adamian _et al._ Ad97 . In order to describe the fusion
dynamics as a diffusion process along proton and neutron degrees of freedom,
the fusion probability is obtained by solving a set of master equations
numerically in the potential energy surface of the DNS. The time evolution of
the distribution probability function $P(Z_{1},N_{1},E_{1},t)$ for fragment 1
with proton number $Z_{1}$ and neutron number $N_{1}$ and with excitation
energy $E_{1}$ is described by the following master equations,

$\displaystyle\frac{dP(Z_{1},N_{1},E_{1},t)}{dt}=$
$\displaystyle\sum_{Z_{1}^{\prime}}W_{Z_{1},N_{1};Z_{1}^{\prime},N_{1}}(t)\left[d_{Z_{1},N_{1}}P(Z_{1}^{\prime},N_{1},E_{1}^{\prime},t)-d_{Z_{1}^{\prime},N_{1}}P(Z_{1},N_{1},E_{1},t)\right]+\sum_{N_{1}^{\prime}}W_{Z_{1},N_{1};Z_{1},N_{1}^{\prime}}(t)$
(2)
$\displaystyle\left[d_{Z_{1},N_{1}}P(Z_{1},N_{1}^{\prime},E_{1}^{\prime},t)-d_{Z_{1},N_{1}^{\prime}}P(Z_{1},N_{1},E_{1},t)\right]-\left[\Lambda_{qf}(\Theta(t))+\Lambda_{fis}(\Theta(t))\right]P(Z_{1},N_{1},E_{1},t).$

Here $W_{Z_{1},N_{1};Z_{1}^{\prime},N_{1}}$
($W_{Z_{1},N_{1};Z_{1},N_{1}^{\prime}}$) is the mean transition probability
from the channel $(Z_{1},N_{1},E_{1})$ to
$(Z_{1}^{\prime},N_{1},E_{1}^{\prime})$ (or $(Z_{1},N_{1},E_{1})$ to
$(Z_{1},N_{1}^{\prime},E_{1}^{\prime})$), and $d_{Z_{1},N_{1}}$ denotes the
microscopic dimension corresponding to the macroscopic state
$(Z_{1},N_{1},E_{1})$. The sum is taken over all possible proton and neutron
numbers that fragment $Z_{1}^{\prime},N_{1}^{\prime}$ may take, but only one
nucleon transfer is considered in the model with the relation
$Z_{1}^{\prime}=Z_{1}\pm 1$ and $N_{1}^{\prime}=N_{1}\pm 1$. The excitation
energy $E_{1}$ is determined by the dissipation energy from the relative
motion and the potential energy surface of the DNS. The motion of nucleons in
the interacting potential is governed by the single-particle Hamiltonian Fe06
; Fe07 . The evolution of the DNS along the variable R leads to the quasi-
fission of the DNS. The quasi-fission rate $\Lambda_{qf}$ and the fission rate
$\Lambda_{fis}$ of heavy fragment are estimated with the one-dimensional
Kramers formula Fe07 ; Fe09 .

In the relaxation process of the relative motion, the DNS will be excited by
the dissipation of the relative kinetic energy. The local excitation energy is
determined by the excitation energy of the composite system and the potential
energy surface of the DNS. The potential energy surface (PES) of the DNS is
given by

$\displaystyle U(\\{\alpha\\})=$ $\displaystyle
B(Z_{1},N_{1})+B(Z_{2},N_{2})-\left[B(Z,N)+V^{CN}_{rot}(J)\right]$ (3)
$\displaystyle+V(\\{\alpha\\})$

with $Z_{1}+Z_{2}=Z$ and $N_{1}+N_{2}=N$. Here the symbol $\\{\alpha\\}$
denotes the sign of the quantities
$Z_{1},N_{1},Z_{2},N_{2};J,\textbf{R};\beta_{1},\beta_{2},\theta_{1},\theta_{2}$.
The $B(Z_{i},N_{i})(i=1,2)$ and $B(Z,N)$ are the negative binding energies of
the fragment $(Z_{i},N_{i})$ and the compound nucleus $(Z,N)$, respectively,
which are calculated from the liquid drop model, in which the shell and the
pairing corrections are included reasonably. The $V^{CN}_{rot}$ is the
rotation energy of the compound nucleus. The $\beta_{i}$ represent the
quadrupole deformations of the two fragments. The $\theta_{i}$ denote the
angles between the collision orientations and the symmetry axes of deformed
nuclei. The interaction potential between fragment $(Z_{1},N_{1})$ and
$(Z_{2},N_{2})$ includes the nuclear, Coulomb and centrifugal parts, the
details are given in Ref. Fe07 . In the calculation, the distance R between
the centers of the two fragments is chosen to be the value which gives the
minimum of the interaction potential, in which the DNS is considered to be
formed. So the PES depends on the proton and neutron numbers of the fragments.

The formation probability of the compound nucleus at the Coulomb barrier $B$
and for the angular momentum $J$ is given by Fe06 ; Fe07

$P_{CN}(E_{c.m.},J,B)=\sum_{Z_{1}=1}^{Z_{BG}}\sum_{N_{1}=1}^{N_{BG}}P(Z_{1},N_{1},E_{1},\tau_{int}).$
(4)

The interaction time $\tau_{int}$ in the dissipation process of two colliding
partners is dependent on the incident energy $E_{c.m.}$ and the quantities $J$
and $B$. We obtain the fusion probability as

$P_{CN}(E_{c.m.},J)=\int f(B)P_{CN}(E_{c.m.},J,B)dB,$ (5)

where the barrier distribution function is taken as an asymmetric Gaussian
form.

Neutron-deficient SHN with charged numbers Z=107-113 were synthesized
successfully in the cold fusion reactions. The evaporation residues was
observed by the consecutive $\alpha$ decay until to take place the spontaneous
fission of the known nuclides, in which the fusion dynamics and the structure
properties of the compound nucleus have a strong influence in the production
of SHN. Recently more neutron-rich and heavier SHN with charged numbers
Z=113-116, 118 were produced in the fusion-evaporation reactions of 48Ca
bombarding actinide targets. Superheavy residues were also identified by the
consecutive $\alpha$ decay, unfortunately to spontaneous fission of unknown
nuclides. Neutron-rich projectile-target combinations are necessary to be
chosen so that superheavy residues approach the ”island of stability” with the
doubly magic shell closure beyond 208Pb at the position of protons Z=114-126
and neutrons N=184. New SHN between the isotopes of the cold fusion and the
48Ca induced reactions are of importance for the structure studies themselves
and also as daughter nuclides for identifying heavier SHN in the future.

Figure 1:  Evaporation residue excitation functions in the production of
isotopes of superheavy element Mt in the reactions 27Al+248,250Cm, 31P+244Pu
and 37Cl+238U. Figure 2:  Calculated production cross sections for the
reactions 30Si+248,250Cm, 36S+244Pu and 40Ar+238U to produce superheavy
element Ds.

The excitation energy of compound nucleus is obtained by
$E^{\ast}_{CN}=E_{c.m.}+Q$, where $E_{c.m.}$ is the incident energy in the
center-of-mass system. The $Q$ value is given by $Q=\Delta M_{P}+\Delta
M_{T}-\Delta M_{C}$, and the corresponding mass defects are taken from Ref.
Mo95  for projectile, target and compound nucleus, respectively. Usually,
neutron-rich projectiles are used to synthesize SHN experimentally, such as
64Ni and 70Zn in the cold fusion reactions, which can enhance the survival
probability $W_{sur}$ in Eq.(1) of the excited compound nucleus owing to
smaller neutron separation energy. Within the framework of the DNS model, we
calculated the evaporation residue cross sections of superheavy element Mt
based on the actinide targets 248,250Cm, 244Pu and 238U with the neutron-rich
projectiles 27Al, 31P and 37Cl as shown in Fig. 1. One can see that the 3n
channel in the reactions 27Al+248Cm and 37Cl+238U, and the 4n and 5n channels
in the system 27Al+250Cm have the larger cross sections in the production of
SHN 272Mt and 273Mt. Superheavy element Ds(Z=110) was successfully synthesized
in the cold fusion reactions Ho00 ; Ho95 . The production of the SHN depends
on the isotopic combinations of projectiles and targets. Calculations were
performed for the reactions 30Si+248,250Cm, 36S+244Pu and 40Ar+238U to produce
superheavy element Ds as shown in Fig. 2. Combination with 248Cm has the
larger cross section in the 4n channel than the isotope 250Cm due to the
larger value of survival probability. The 4n channels in the systems
30Si+248,250Cm and 40Ar+238U and the 3n channel in the reaction 30Si+248Cm are
feasible in the synthesis of new SHN 274-276Ds. These combinations can be
chosen in experimental preparation with the present facilities.

Figure 3:  The same as in Fig. 1, but for the reactions 31P+248,250Cm,
37Cl+244Pu and 41K+238U to produce superheavy element Rg. Figure 4:
Comparison of the calculated production cross sections in the reactions
36S+248,250Cm and 40Ar+244Pu to synthesize superheavy element Z=112.

In the DNS model, the isotopic trends are mainly determined by both the fusion
and survival probabilities. When the neutron number of the projectile is
increasing, the DNS gets more symmetrical and the fusion probability decreases
if the DNS does not consist of more stable nuclei due to a higher inner fusion
barrier. A smaller neutron separation energy and a larger shell correction
lead to a larger survival probability. The compound nucleus with closed
neutron shells has larger shell correction energy and neutron separation
energy. The cross section decreases rapidly in the production of the isotopes
of Rg(Z=111). Optical channels are the 4n evaporation for the systems
31P+248,250Cm, 37Cl+244Pu and 41K+238U to produce 275,277Rg as shown in Fig.
3. Superheavy element Z=112 is more difficult to be produced in the selected
systems. Shown in Fig. 5 gives that the possible way is the 4n channel in the
reaction 36S+250Cm, but the cross section is still smaller than the system
48Ca+238U Fe09  although the larger mass asymmetry.

Table 1:  Comparisons of calculated maximal evaporation residue cross sections and optimal excitation energies (in bracket) in 2n-5n channels. | Reactions |  $\sigma_{ER}^{2n}$(pb) ($E^{\ast}_{CN}$) |  $\sigma_{ER}^{3n}$(pb) ($E^{\ast}_{CN}$) |  $\sigma_{ER}^{4n}$(pb) ($E^{\ast}_{CN}$) |  $\sigma_{ER}^{5n}$(pb) ($E^{\ast}_{CN}$)
---|---|---|---|---|---
|  26Mg+248Cm | 2.50 (40 MeV) | 26.2 (40 MeV) | 719.1 (42 MeV) | 1.23 (51 MeV)
|  26Mg+250Cm | 1.11 (41 MeV) | 10.57 (41 MeV) | 185.2 (42 MeV) | 108.5 (45 MeV)
|  30Si+244Pu | 0.46 (42 MeV) | 5.09 (43 MeV) | 185.1 (44 MeV) | 0.72 (51 MeV)
|  36S+238U | 0.21 (37 MeV) | 1.96 (38 MeV) | 42.97 (42 MeV) | 0.11 (52 MeV)
|  27Al+248Cm | 0.31 (44 MeV) | 27.83 (44 MeV) | 3.59 (47 MeV) | 1.34 (51 MeV)
|  27Al+250Cm | 0.12 (46 MeV) | 1.64 (46 MeV) | 24.31 (46 MeV) | 97.44 (49 MeV)
|  31P+244Pu | 4.71$\times 10^{-2}$ (47 MeV) | 4.25 (47 MeV) | 0.87 (50 MeV) | 0.52 (53 MeV)
|  37Cl+238U | 0.16 (38 MeV) | 13.31 (38 MeV) | 0.67 (44 MeV) | 0.17 (50 MeV)
|  30Si+248Cm | 0.34 (39 MeV) | 1.72 (39 MeV) | 65.32 (43 MeV) | 1.22$\times 10^{-2}$ (56 MeV)
|  30Si+250Cm | 0.11 (41 MeV) | 0.42 (42 MeV) | 3.54 (44 MeV) | 0.93 (48 MeV)
|  36S+244Pu | 3.87$\times 10^{-2}$ (36 MeV) | 0.101 (38 MeV) | 0.61 (41 MeV) | 0.12 (48 MeV)
|  40Ar+238U | 0.26 (32 MeV) | 0.55 (36 MeV) | 2.10 (42 MeV) | 0.45 (53 MeV)
|  31P+248Cm | 4.11$\times 10^{-2}$ (43 MeV) | 0.31 (44 MeV) | 1.85 (45 MeV) | 0.69 (50 MeV)
|  31P+250Cm | 9.91$\times 10^{-3}$ (47 MeV) | 5.49$\times 10^{-2}$ (48 MeV) | 0.41 (51 MeV) | 0.25 (52 MeV)
|  37Cl+244Pu | 4.01$\times 10^{-2}$ (36 MeV) | 0.11 (38 MeV) | 0.33 (42 MeV) | 7.15$\times 10^{-2}$ (49 MeV)
|  41K+238U | 1.96$\times 10^{-2}$ (32 MeV) | 8.67$\times 10^{-2}$ (35 MeV) | 0.21 (41 MeV) | 3.77$\times 10^{-2}$ (49 MeV)
|  36S+248Cm | 4.31$\times 10^{-2}$ (33 MeV) | 7.64$\times 10^{-2}$ (36 MeV) | 7.02$\times 10^{-2}$ (43 MeV) | 2.55$\times 10^{-3}$ (54 MeV)
|  36S+250Cm | 1.76$\times 10^{-2}$ (36 MeV) | 8.25$\times 10^{-2}$ (37 MeV) | 0.24 (42 MeV) | 1.47$\times 10^{-2}$ (51 MeV)
|  40Ar+244Pu | 5.79$\times 10^{-3}$ (31 MeV) | 9.48$\times 10^{-3}$ (36 MeV) | 9.84$\times 10^{-3}$ (44 MeV) | 5.02$\times 10^{-4}$ (56 MeV)
|  37Cl+248Cm | 5.81$\times 10^{-2}$ (31 MeV) | 0.26 (35 MeV) | 0.195 (42 MeV) | 1.05$\times 10^{-2}$ (52 MeV)
|  37Cl+250Cm | 2.08$\times 10^{-2}$ (35 MeV) | 0.21 (36 MeV) | 0.594 (41 MeV) | 7.06$\times 10^{-2}$ (49 MeV)
|  41K+244Pu | 1.11$\times 10^{-2}$ (29 MeV) | 4.22$\times 10^{-2}$ (34 MeV) | 3.31$\times 10^{-2}$ (42 MeV) | 2.3$\times 10^{-3}$ (53 MeV)
|  45Sc+238U | 1.72$\times 10^{-2}$ (27 MeV) | 1.99$\times 10^{-2}$ (35 MeV) | 2.32$\times 10^{-3}$ (45 MeV) | 1.92$\times 10^{-4}$ (57 MeV)
|  40Ar+248Cm | 6.98$\times 10^{-3}$ (26 MeV) | 2.21$\times 10^{-2}$ (33 MeV) | 3.5$\times 10^{-2}$ (41 MeV) | 2.12$\times 10^{-3}$ (51 MeV)
|  40Ar+250Cm | 2.77$\times 10^{-3}$ (29 MeV) | 2.11$\times 10^{-2}$ (33 MeV) | 7.96$\times 10^{-2}$ (40 MeV) | 9.69$\times 10^{-3}$ (48 MeV)
|  48Ti+238U | 4.51$\times 10^{-2}$ (24 MeV) | 1.37$\times 10^{-2}$ (32 MeV) | 5.81$\times 10^{-3}$ (42 MeV) | 1.71$\times 10^{-4}$ (54 MeV)
|  50Ti+238U | 5.11$\times 10^{-2}$ (23 MeV) | 2.18$\times 10^{-2}$ (31 MeV) | 1.07$\times 10^{-2}$ (40 MeV) | 4.11$\times 10^{-4}$ (50 MeV)

The productions of superheavy element Z=113 were successfully performed in the
cold fusion reaction 70Zn+209Bi Mo04  and also in the hot fusion 48Ca+237Np
Og07b  with the cross section less than 1 pb. We calculated the evaporation
residue excitation functions for the reactions 37Cl+248,250Cm, 41K+244Pu and
45Sc+238U. The results show that the 3n and 4n channels in the systems
37Cl+248,250Cm have larger cross sections and are possible to synthesize new
isotopes 281-284113 in experimentally. Superheavy element Z=114 is difficulty
to be produced from our calculations for the selected systems because of the
smaller cross sections with less than 0.1 pb for all systems. We list the
maximal production cross sections and the corresponding excitation energies in
the brackets calculated by using the DNS model in Table 1. These selected
systems and evaporation channels are feasible to produce new isotopes between
the cold fusion and the 48Ca induced fusion reactions.

In summary, we systematically investigated the production of superheavy
residues in the fusion-evaporation reactions using the DNS model, in which the
nucleon transfer leading to the formation of superheavy compound nucleus is
described by a set of microscopically derived master equations distinguishing
the proton and neutron transfer that are coupled to the dissipation of
relative motion energy and angular momentum. The production of new isotopes
between the gap of the cold fusion and the 48Ca induced fusion reactions are
discussed for selected systems. Optimal evaporation channels and excitation
energies corresponding to the maximal cross sections are stated and discussed
systematically.

###### Acknowledgements.

We would like to thank Prof. Werner Scheid for carefully reading the
manuscript. This work was supported by the National Natural Science Foundation
of China under Grant Nos. 10805061 and 10775061, the special foundation of the
president fund, the west doctoral project of Chinese Academy of Sciences, and
major state basic research development program under Grant No. 2007CB815000.

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