A Collaborative Swarm-Differential Evolution Algorithm for Multi-Objective Multi-Robot Task Assignment
Year: 2025
Type: article
Abstract: Multi-Robot Task Assignment (MRTA) is a critical and inherently multi-objective problem in diverse real-world applications, demanding the simultaneous optimization of conflicting objectives such as minimizing total travel distance and balancing robot workload. Existing multi-objective evolutionary algorithms (MOEAs) often struggle with slow convergence and insufficient diversity when tackling the combinatorial complexity of large-scale MRTA instances. This paper introduces the Collaborative Swarm-Differential Evolution (CSDE) algorithm, a novel MOEA designed to overcome these limitations. CSDE’s core innovation lies in its deep, operator-level fusion of Differential Evolution’s (DE) robust global exploration capabilities with Particle Swarm Optimization’s (PSO) swift local exploitation prowess. This is achieved through a unique fused velocity update mechanism, enabling particles to dynamically benefit from their personal experience, collective swarm intelligence, and population diversity-driven knowledge transfer. Comprehensive experiments on various MRTA scenarios demonstrate that CSDE consistently achieves superior performance in terms of convergence, solution diversity, and Pareto front quality, significantly outperforming standard multi-objective algorithms like Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Differential Evolution (MODE), and Multi-Objective Genetic Algorithm (MOGA). This study highlights CSDE’s substantial contribution to the MRTA field and its potential for more effective and efficient multi-robot system deployment.
Source: Applied Sciences
DOI: 10.3390/app151910627
Authors: Zhaohui Zhang, Wanqiu Zhao, Xu Bian, Hong Zhao
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4414650995
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