计算机应用研究2024，Vol.41Issue(6)：1640-1648,9.DOI:10.19734/j.issn.1001-3695.2023.09.0502
基于相对距离和历史成功率机制的增强麻雀搜索算法
Enhanced sparrow search algorithm by adopting mechanism based on relative distance and historical success rate
摘要
Abstract
Aiming to overcome faults of lower convergence accuracy and susceptibility to local optima in sparrow search algo-rithm(SSA),this paper proposed an enhanced sparrow search algorithm by adopting the mechanism based on relative distance and historical success rate,namely RHSSA.Firstly,RHSSA introduced a discoverer selection method that integrated fitness values and relative distance to make selected discoverers maintaining high quality and wider distribution in search space.Secondly,RHSSA adopted a reverse learning strategy that integrated weighted center of gravity during each search iteration of discovers in order to fully mining the high-quality location information in the search space and weakening discoverers'trend to gather towards the origin.Finally,RHSSA also used an adaptive selection operator based on historical success rate to dynami-cally select between Cauchy and Gaussian mutations to disturb the optimal solution to improve the algorithm's ability to jump out of local optimal.12 functions were selected from the CEC2017 test function suit as the benchmark to evaluate RHSSA with five other improved sparrow search algorithms(AMSSA,SCSSA,SHSSA,ISSA,and CSSOA).The result of Friedman test based on experimental data shows that RHSSA can achieve the supreme performance among all evaluated algorithms.To futher verify effectiveness of the proposed improvement strategies,ablation experiments were conducted.The result illustrates that un-der the combination of all proposed improvement strategies,RHSSA ranks first in comprehensive optimization performance.关键词
麻雀搜索算法/适应度值与相对距离/加权重心/反向学习/自适应选择算子Key words
sparrow search algorithm/fitness value and relative distance/weighted center of gravity/opposition-based lear-ning/adaptive selection operator分类
信息技术与安全科学引用本文复制引用
李大海,曾能智,王振东..基于相对距离和历史成功率机制的增强麻雀搜索算法[J].计算机应用研究,2024,41(6):1640-1648,9.基金项目
国家自然科学基金资助项目(61563019,615620237) （61563019,615620237）
江西理工大学校级基金资助项目(205200100013) （205200100013）
