阶段化改进的海洋捕食者算法及其应用  

Phased-improvement marine predators algorithm and its application

作　　者：付华[1] 刘尚霖 管智峰 刘昊 FU Hua;LIU Shang-lin;GUAN Zhi-feng;LIU Hao(Faculty of Electrical and Control Engineering,Liaoning Technical University,Huludao 125105,China)

机构地区：[1]辽宁工程技术大学电气与控制学院,辽宁葫芦岛125105

出　　处：《控制与决策》2023年第4期902-910,共9页Control and Decision

基　　金：国家自然科学基金项目(51974151,71771111);辽宁省高等学校国(境)外培养项目(2019GJWZD002);辽宁省高等学校创新团队项目(LT2019007);辽宁省教育厅科技项目(LJ2019QL015)。

摘　　要：针对海洋捕食者算法自适应能力有限、局部桎梏概率高等不足,提出阶段化改进的海洋捕食者算法.在高速度比阶段实施差分演化,在原始猎物群体的变异与交叉中扩大全局探索范围,遍历优化个体质量;引入正余弦算法等概率波动等速度比阶段的并行架构,提升莱维飞行群体与布朗运动群体的灵活性,促进群体间渗透,同节奏优化算法的开发能力与探索能力;融合柯西变异策略与反向学习策略改进低速度比阶段捕食者,生成具备自我调节能力的柯西镜像捕食者,避免迭代末期种群同化过度,强化算法反早熟能力.通过基准函数对比寻优实验及Wilcoxon符号秩检验来评估改进算法的性能,实验结果验证了阶段化改进策略对算法整体表现力的提升.利用改进算法优化在线序列极限学习机参数并应用于变压器故障诊断,进一步验证阶段化改进策略的有效性及工程实用性.Aiming at the disadvantages of limited adaptive ability and high local shackles probability of marine predators algorithm,a phased-improvement marine predators algorithm is proposed.The differential evolution is implemented in the high velocity ratio phase,which expands the global exploration range with the variation and crossover of the original prey population,and optimize the individual quality seriatim.The sine and cosine algorithm is introduced in the unit velocity phase to fluctuate the parallel architecture with an equal probablity,which is used to improve the flexibility of the Levy flight group and the Brownian motion group,and promote the penetration between groups in order to optimize the development and exploration capabilities of the algorithm in the same rhythm.The Cauchy mutation strategy and the reverse learning strategy are combined in the low velocity ratio phase to evolve the predators into the self-regulation Cauchy mirror predators,for avoiding excessive population assimilation at the end of iteration,so that the anti precocity ability of the algorithm is strengthened.The performance of the improved algorithm is evaluated by the benchmark function comparison optimization experiments and the Wilcoxon’s sign rank test,and the results verify that the phased-improvement strategy contributes to the overall expressiveness of the algorithm.The improved algorithm is used to optimize the paraments of online sequential extreme learning machine and applied to transformer fault diagnosis,and the effectiveness and engineering practicability of the phased-improvement strategy are further verified.

关 键 词：智能优化算法 海洋捕食者算法 阶段化改进 变压器故障诊断 在线序列极限学习机 

分 类 号：TD713[矿业工程—矿井通风与安全]