基于个性化策略改进捕鱼算法的光伏电站 MPPT 优化

Improved Catch Fish Optimization Algorithm with Personalized Strategy for Photovoltaic MPPT

摘要

针对捕鱼优化算法(CFOA: Catch Fish Optimization Algorithm)在探索与开发 2 个更新阶段容易陷入局部最优且收敛速度较低的问题, 基于个性化捕鱼策略提出改进型捕鱼优化算法(ICFOA: Improved Catch Fish Optimization Algorithm)。首先, 在探索阶段引入自适应高斯扰动, 在提升全局搜索能力与效率的同时避免陷入局部最优。其次, 基于个性化捕鱼策略, 通过随机选择“徒手捕鱼”因子或“使用渔网”因子更新渔民位置, 以加速算法收敛。最后, 利用 CEC2020 测试套件进行对比实验, 评估 ICFOA 与其他优秀元启发式算法的性能差异, 并使用 Wilcoxon 秩和检验验证统计结果的有效性。其在光伏电站最大功率点跟踪应用结果表明, ICFOA 能有效降低功率追踪差值。证明了其解决实际问题的有效性, 相较原始 CFOA 具有更强的竞争力。

Abstract

CFOA(Catch Fish Optimization Algorithm) usually includes two update stages, the exploration stage and the exploitation stage. However, this algorithm is still prone to getting stuck in local optima and has a relatively low convergence rate. To address these issues, the ICFOA (Improved Catch Fish Optimization Algorithm) is proposed based on personalized fishing strategies. Firstly, an adaptive Gaussian perturbation is introduced in the exploration stage to enhance the global search ability and efficiency while avoiding local optima. Secondly, based on personalized fishing strategies, the positions of fishermen are updated by randomly selecting either the “bare-handed fishing” factor or the “using a fishing net” factor to accelerate the convergence. The CEC2020 test suite is used to conduct comparative experiments to evaluate the performance differences between ICFOA and other excellent meta-heuristic algorithms, and the Wilcoxon rank sum test is used to verify the validity of the statistical results. Finally, the maximum power point tracking in photovoltaic power plants, ICFOA effectively reduces the power tracking deviation, demonstrating its effectiveness in solving practical problems. Experimental results indicate that ICFOA possesses greater competitiveness compared to the original CFOA.

关键词

捕鱼优化算法 / 元启发式算法 / 群体智能 / 个性化捕鱼策略

Key words

catch fish optimization algorithm / meta-heuristic algorithms / swarm intelligence / personalized fishing strategy

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基金资助

黑龙江省重点研发计划基金资助项目(2024ZXJ01A04)