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Self-adaptive differential evolution-based coati optimization algorithm for multi-robot path planning

Published online by Cambridge University Press:  03 February 2025

Lun Zhu ,
Guo Zhou ,
Yongquan Zhou Open the ORCID record for Yongquan Zhou [Opens in a new window] ,
Qifang Luo ,
Huajuan Huang  and
Xiuxi Wei
Lun Zhu
Affiliation:
College of Artificial Intelligence, Guangxi Minzu University, Nanning, China
Guo Zhou
Affiliation:
Department of Science and Technology Teaching, China University of Political Science and Law, Beijing, China
Yongquan Zhou*
Affiliation:
College of Artificial Intelligence, Guangxi Minzu University, Nanning, China Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning, China
Qifang Luo
Affiliation:
College of Artificial Intelligence, Guangxi Minzu University, Nanning, China Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning, China
Huajuan Huang
Affiliation:
College of Artificial Intelligence, Guangxi Minzu University, Nanning, China Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning, China
Xiuxi Wei
Affiliation:
College of Artificial Intelligence, Guangxi Minzu University, Nanning, China Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning, China
*
Corresponding author: Yongquan Zhou; Email: zhouyongquan@gxun.edu.cn
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Abstract

The multi-robot path planning problem is an NP-hard problem. The coati optimization algorithm (COA) is a novel metaheuristic algorithm and has been successfully applied in many fields. To solve multi-robot path planning optimization problems, we embed two differential evolution (DE) strategies into COA, a self-adaptive differential evolution-based coati optimization algorithm (SDECOA) is proposed. Among these strategies, the proposed algorithm adaptively selects more suitable strategies for different problems, effectively balancing global and local search capabilities. To validate the algorithm’s effectiveness, we tested it on CEC2020 benchmark functions and 48 CEC2020 real-world constrained optimization problems. In the latter’s experiments, the algorithm proposed in this paper achieved the best overall results compared to the top five algorithms that won in the CEC2020 competition. Finally, we applied SDECOA to optimization multi-robot online path planning problem. Facing extreme environments with multiple static and dynamic obstacles of varying sizes, the SDECOA algorithm consistently outperformed some classical and state-of-the-art algorithms. Compared to DE and COA, the proposed algorithm achieved an average improvement of 46% and 50%, respectively. Through extensive experimental testing, it was confirmed that our proposed algorithm is highly competitive. The source code of the algorithm is accessible at: https://ww2.mathworks.cn/matlabcentral/fileexchange/164876-HDECOA.

Keywords

differential evolutioncoati optimization algorithmself-adaptive differential evolution-based coati optimizationmulti-robot path planningmetaheuristic
Type
Research Article
Information
Robotica , First View , pp. 1 - 38
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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