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A hypervolume-based evolutionary algorithm for rescue robot assignment problem of nuclear accident

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Abstract

Robots usually carry out rescue tasks after nuclear accidents due to harsh environmental conditions such as radiation, high temperatures, and pressure. The assignment of rescue robots is a crucial aspect of this study, serving as a preliminary step in multi-robot task allocation. Its primary objective is to allocate robots to different groups based on the distribution of tasks. This paper models robot assignment as a multi-objective optimization problem by considering total execution time, regional load balance degree, and total transfer time. An improved hypervolume estimated multi-objective evolutionary algorithm (IHypE) is proposed. An encoding method is devised to represent the assignment of the robots. A corner-point-based hypervolume approximation method is proposed to efficiently measure the quality of each solution. An environmental selection that is more applicable to this problem is developed. In the experimental section, we conduct a comparative analysis between the proposed method and four state-of-the-art algorithms, namely MMODE-ICD, MOEA/D-2TH, NSGA-II, and SPEA2SDE. This comparison is performed on nine instances with varying scales. By evaluating the algorithm’s performance using five evaluation indicators, including hypervolume, inverse generational distance, C-Metric, Spacing, and Spread, we demonstrate the competitiveness of the proposed algorithm in terms of convergence and diversity.

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Acknowledgements

This work is supported by the Key Program of National Natural Science Foundation of China (NSFC) under Grant U22B2058, U2013602 and Grant No. 62102071, the Natural Science Foundation of Sichuan Province (Grant No. 2022NSFSC0873), and the Hong Kong Scholars Program (Project No. XJ2021007).

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Authors and Affiliations

  1. School of Automation, Beijing Institute of Technology, Beijing, 100081, China

    Chengxin Wen & Lihua Li

  2. Department of Computer Science, City University of Hong Kong, Hong Kong, China

    Peiqiu Huang & Shaolong Shi

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  1. Chengxin Wen
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  2. Peiqiu Huang
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Contributions

Chengxin Wen: Methodology, Software, Validation, Investigation. Peiqiu Huang: Validation, Formal analysis, Writing - review & editing. Shaolong Shi: Validation, Writing - review & editing. Lihua Li: Visualization.

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Correspondence to Chengxin Wen.

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This manuscript has not been published or presented elsewhere and is not under consideration by another journal. We have read and understood your journal’s policies, and we believe that neither the manuscript nor the study violates any of these. All authors have checked the manuscript and agreed to the submission. There are no conflicts of interest to declare. The data that support the findings of this study are available from the author, Chengxin Wen, upon reasonable request.

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Peiqiu Huang, Shaolong Shi and Lihua Li are contributed equally to this work.

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Wen, C., Huang, P., Shi, S. et al. A hypervolume-based evolutionary algorithm for rescue robot assignment problem of nuclear accident. Appl Intell 53, 27912–27933 (2023). https://doi.org/10.1007/s10489-023-04984-9

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