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

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 multi-graph 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

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