多策略融合改进的均衡优化算法及其应用

A multi-strategy fused equilibrium optimization algorithm and its application

针对均衡优化(EO)算法寻优过程中存在兼顾全局探索和局部开发能力弱、寻优精度低、易陷入局部最优等问题,提出多策略融合改进的均衡优化算法(MEO)。首先,采用高破坏性多项式突变策略初始化种群,提高初始阶段解的质量,为全局寻优奠定基础;其次,提出差分变异的重构均衡池策略,丰富迭代过程中种群的多样性,增强算法规避局部最优的能力;同时,采用S型变换因子平衡算法的全局探索和局部开发能力;最后,引入动态螺旋搜索策略,扩大算法的搜索范围,提高算法的收敛精度和速度。仿真实验将MEO算法与标准EO算法以及其他元启发式算法在8个基准测试函数上进行寻优比较,实验结果与Wilcoxon秩和检验结果均表明,本文改进策略能提高EO算法的寻优精度、全局探索和局部开发的能力以及跳出局部最优的能力。另外,将MEO算法应用在无线传感器网络(WSN)覆盖优化中,实验结果表明,MEO算法可以显著提高WSN的覆盖率,降低节点的冗余度,使节点分布更均匀。

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.