改进的蜉蝣优化算法的经济负荷分配

Enhanced Mayfly Optimization Algorithm for Economic Load Dispatch Problems

针对电力系统经济负荷分配问题的多维、非线性、不可导和多约束等特性,提出了一种基于精英策略改进的蜉蝣优化算法。6个标准测试函数的测试结果显示该算法具备优秀的勘探和开采平衡性,有效避免过早收敛,同时具有高搜索精度和优化鲁棒性等特点。最后选取三个经典案例进行实证研究,并与遗传算法和粒子群优化算法进行对比,结果表明该算法能够有效解决电力系统的经济负荷分配问题,降低总发电成本并减少碳排放。

Considering the multidimensional,nonlinear,non-differentiable,and multi-constraint characteristics of the economic load dispatch problems in power systems,this study proposes an enhanced mayfly optimization algorithm(MA)incorporating an elitist strategy.The test results of six standard test functions demonstrate that the algorithm exhibits excellent exploration and exploitation balance,effectively avoiding premature convergence,and achieves high search accuracy and optimization robustness.Through empirical investigations on three representative case studies and a comparative analysis with genetic algorithm(GA)and particle swarm optimization(PSO),the experimental results validate the effectiveness of the proposed algorithm in addressing the economic load dispatch problems in power systems.The algorithm not only reduces the overall power generation cost but also contributes to the reduction of carbon emissions.