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Open Access

ARTICLE

EG-IGGO: An Evolutionary Game-Improved Greylag Goose Optimization Algorithm for Multi-Robot Path Planning

Ao Nie1, Wei Zhou1, Yi Yu1, Wan Xu1,2,*
1 School of Mechanical Engineering, Hubei University of Technology, Wuhan, China
2 Hubei Key Laboratory of Modern Manufacturing Quality Engineering, Hubei University of Technology, Wuhan, China
* Corresponding Author: Wan Xu. Email: email
(This article belongs to the Special Issue: Advances in Bio-Inspired Optimization Algorithms: Theory, Algorithms, and Applications)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.083871

Received 12 April 2026; Accepted 09 July 2026; Published online 11 August 2026

Abstract

Currently, mobile robot path planning in unstructured forest environments remains a hot research topic in the robotics field. Studies applying the Greylag Goose Optimization (GGO) algorithm to multi-robot path planning under such scenarios are limited, and these approaches still face significant challenges, such as insufficient trajectory smoothness, frequent coordination conflicts, and relatively slow convergence to optimal solutions. To address these issues, this paper proposes an Evolutionary Game-Theoretic Improved GGO algorithm (EG-IGGO), designed to optimize path quality while ensuring robust obstacle avoidance capabilities. Specifically, two novel strategies—the population alignment strategy and the dual-source adaptive guidance strategy—are integrated into the original GGO framework. The population alignment strategy refines solution quality and enhances trajectory smoothness; the dual-source adaptive guidance strategy balances global exploration and local exploitation, thereby reducing collision risks and mitigating coordination conflicts. Furthermore, to accelerate convergence, an evolutionary game algorithm is introduced to dynamically adjust the probabilities of strategy selection, ensuring individuals consistently adopt the optimal strategy for efficient and robust optimal path search. Rigorous comparative evaluations were conducted using the CEC2022 benchmark functions, where the performance of EG-IGGO was assessed against advanced algorithms including PSO, GWO, GGO, FAPSO-GM, and FSDBWO. Results demonstrate that EG-IGGO outperforms these comparative algorithms and their variants across multiple metrics. Experimental validation in three forest environments with different complexity levels further confirms its effectiveness; compared with existing methods, EG-IGGO achieves a 21.37% improvement in convergence speed and a 28.25% improvement in path smoothness. This study provides a novel theoretical framework and a high-performance solution for multi-robot path planning in complex environments.

Keywords

Evolutionary game; multi-robot; greylag goose optimization algorithm; path plan

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