Visible to the public Multi-objective Gray Wolf Optimization Algorithm for Multi-agent Pathfinding Problem

TitleMulti-objective Gray Wolf Optimization Algorithm for Multi-agent Pathfinding Problem
Publication TypeConference Paper
Year of Publication2022
AuthorsWei, Lianghao, Cai, Zhaonian, Zhou, Kun
Conference Name2022 IEEE 5th International Conference on Electronics Technology (ICET)
Date Publishedmay
Keywordscomposability, compositionality, Computational modeling, Heuristic algorithms, Linear programming, multi-agent pathfinding, multi-objective gray wolf optimization, particle swarm optimization, pubcrawl, security, Sociology, Statistics, swarm intelligence
AbstractAs a core problem of multi-agent systems, multiagent pathfinding has an important impact on the efficiency of multi-agent systems. Because of this, many novel multi-agent pathfinding methods have been proposed over the years. However, these methods have focused on different agents with different goals for research, and less research has been done on scenarios where different agents have the same goal. We propose a multiagent pathfinding method incorporating a multi-objective gray wolf optimization algorithm to solve the multi-agent pathfinding problem with the same objective. First, constrained optimization modeling is performed to obtain objective functions about agent wholeness and security. Then, the multi-objective gray wolf optimization algorithm is improved for solving the constrained optimization problem and further optimized for scenarios with insufficient computational resources. To verify the effectiveness of the multi-objective gray wolf optimization algorithm, we conduct experiments in a series of simulation environments and compare the improved multi-objective grey wolf optimization algorithm with some classical swarm intelligence optimization algorithms. The results show that the multi-agent pathfinding method incorporating the multi-objective gray wolf optimization algorithm is more efficient in handling multi-agent pathfinding problems with the same objective.
DOI10.1109/ICET55676.2022.9824428
Citation Keywei_multi-objective_2022