AccScience Publishing / IJOCTA / Online First / DOI: 10.36922/IJOCTA025280120
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RESEARCH ARTICLE

Multi-graph network population evolutionary optimization algorithm with migration and best hunter crossover strategies for cross-field applications

Zhaoyang Lian1 ,  Bailu Si1*
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1 School of Systems Science, Beijing Normal University, Beijing, China
IJOCTA, 025280120 https://doi.org/10.36922/IJOCTA025280120
Received: 7 July 2025 | Revised: 27 August 2025 | Accepted: 22 September 2025 | Published online: 29 October 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Although swarm intelligence optimization algorithms, such as simulating biological bionic behaviors or natural laws have been relatively mature, there are relatively few algorithms considering multi-graph network evolutionary behaviors and the algorithms combining graph network structure with biomimetic behaviors are worth studying. In this paper, a multi-graph network population optimization algorithm with migration and best hunter crossover strategies was proposed for cross-field applications. The gorgeous central radial multigraph matrices were rotated and deformed to change different formations while hunting prey. The global graph population of a strong group was adopted to explore prey in a large range and the local graph population of a weak group was adopted to guard food in a small range near their prey or home. The migration strategy was aimed at reducing overexploitation by hunters and the best hunter crossover strategy was aimed to retain the excellent genes of the best hunter while also preserving the vitality of new individuals. Furthermore, the proposed algorithm was applied to open-source function optimization problems, and extended to four engineering applications and design problems such as multi-sector aviation scheduling, flexible workshop scheduling optimization, unmanned aerial vehicle routing optimization of oil plants in three-dimensional maps, and power system bus type optimization achieving competitive results.

Keywords
Swarm intelligence algorithm
Multi-graph network
Evolutionary optimization algorithm
Cross field applications
Heuristic algorithm
Funding
This work was supported by National Science and Technology Innovation 2030 Major Program of China (2022ZD0205005) and (National Natural Science Foundation of China (42576280).
Conflict of interest
The authors declare no conflict of interest.
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An International Journal of Optimization and Control: Theories & Applications, Electronic ISSN: 2146-5703 Print ISSN: 2146-0957, Published by AccScience Publishing
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