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research-article

A hybrid optimization algorithm for multi-agent dynamic planning with guaranteed convergence in probability

Authors:

Jingyu WangAuthors Info & Claims

Volume 592, Issue C

https://doi.org/10.1016/j.neucom.2024.127764

Published: 24 July 2024 Publication History

Abstract

The paper aims to solve the problem of multi-agent path planning in complex environment using optimization algorithm. To address the issue of local optimum and premature convergence, a new method is proposed based on the whale optimization algorithm, combining the chaotic initialization, the reverse search and the differential evolution methods. It is theoretically proved that this algorithm is globally convergent in probability. When applied to path planning problems, the proposed optimization algorithm can effectively find a globally optimal and smoother path. Through simulation experiments with multi-UAVs, it is demonstrated that the proposed algorithm has better performance than the state-of-the-art methods in environment with both static and dynamic obstacles, reflecting the global convergence and robustness of the proposed algorithm.

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Published In

cover image Neurocomputing

Neurocomputing Volume 592, Issue C

Aug 2024

237 pages

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Copyright © 2024.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 24 July 2024

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