Dynamic Heterogeneous Search-Mutation Structure-Based Equilibrium Optimizer
Xiangdong Wu, Kaoru Hirota, Yaping Dai, Shuai Shao · Applied Sciences · 2025
Aiming at the issues of population diversity attenuation, insufficient search efficiency, and susceptibility to a local optimum in the equilibrium optimizer (EO), a dynamic heterogeneous search-mutation structure-based equilibrium optimizer (DHSMEO) is developed. First of all, a dynamic dual-subpopulation adaptive grouping strategy is constructed to boost population diversity, and it provides an effective information-exchange structure for the heterogeneous hybrid search strategy. Then, a heterogeneous hybrid search-based concentration-updating strategy is integrated to enhance search efficiency. Finally, a dynamic Levy mutation-based optimal equilibrium candidate-refining strategy is incorporated to strengthen the capability of escaping local optima. The optimization capability of DHSMEO is evaluated using 39 typical benchmark functions, and the experimental results validate its effectiveness and superiority. Moreover, the practicality of DHSMEO in solving the practical optimization problem is validated through the UAV mountain path planning problem.