关键词: 均衡优化算法, 高破坏性多项式突变, 差分变异的重构均衡池, S型变换因子, 动态螺旋搜索, 无线传感器网络

Abstract: A multiple-strategy fused equilibrium optimization algorithm (MEO) is proposed to address the problems of weak global exploration and local exploitation ability, low optimization accuracy, and easy fall into local optima during the optimization process of the equilibrium optimization (EO) algorithm. Firstly, a high-destructive polynomial mutation strategy is used to initialize the population to improve the quality of the initial solutions and lay a foundation for global optimization. Secondly, a differential mutation-based reconstruction balanced pool strategy is proposed to enrich the diversity of the population during the iterative process and enhance the algorithms ability to avoid local optima. At the same time, the S-shaped transformation factor balancing algorithm is used to balance the global exploration and local exploitation abilities. Finally, a dynamic spiral search strategy is introduced to expand the search range of the algorithm and improve its convergence accuracy and speed. Simulation experiments are conducted to compare the MEO algorithm with the standard EO algorithm and other metaheuristic algorithms on eight benchmark test functions. The experimental results and Wilcoxon rank-sum test results both show that the proposed improvement strategies can improve the optimization accuracy, global exploration and local exploitation abilities, and the ability to escape from local optima of the EO algorithm. In addition, the MEO algorithm is applied to wireless sensor network (WSN) coverage optimization, and the experimental results show that the MEO algorithm can significantly improve the coverage rate of WSN, reduce the redundancy of nodes, and make node distribution more uniform. This demonstrates that the MEO algorithm can be applied to practical problems and has certain practical value. 

Key words: equilibrium optimization algorithm, highly destructive polynomial mutation, reconstruction of equilibrium pool strategy based on differential mutation, S-shaped transformation factor, dynamic spiral search, wireless sensor network