西安电子科技大学学报（自然科学版）2024，Vol.51Issue(2)：137-156,20.DOI:10.19665/j.issn1001-2400.20230602
融合模式搜索的蝗虫优化算法及其应用
Integration of pattern search into the grasshopper optimization algorithm and its applications
摘要
Abstract
In the process of applying intelligent optimization algorithms to solve complex optimization problems,balancing exploration and exploitation is of great significance in order to obtain optimal solutions.Therefore,this paper proposes a grasshopper optimization algorithm that integrates pattern search to address the limitations of traditional grasshopper optimization algorithm,such as low convergence accuracy,weak search capability,and susceptibility to local optima in handling complex optimization problems.First,a Sine chaotic mapping is introduced to initialize the positions of individual grasshopper population,reducing the probability of individual overlap and enhancing the diversity of the population in the early iterations.Second,the pattern search method is employed to perform local search for the currently found optimal targets in the population,thereby improving the convergence speed and optimization accuracy of the algorithm.Additionally,to avoid falling into local optima in the later stages of the algorithm,a reverse learning strategy based on the imaging of convex lenses is introduced.In the experimental section,a series of ablative experiments is conducted on the improved grasshopper algorithm to validate the independent effectiveness of each strategy,including the Sine chaotic mapping,pattern search,and reverse learning.Simulation experiments are performed on two sets of test functions,with the results analyzed using the Wilcoxon rank-sum test and Friedman test.Experimental results consistently demonstrate that the fusion mode search strategy improved grasshopper algorithm exhibits significant enhancements in both convergence speed and optimization accuracy.Furthermore,the application of the improved algorithm to mobile robot path planning further validates its effectiveness.关键词
蝗虫优化算法/粒子群优化算法/模式搜索/时间复杂度/统计检验/路径规划Key words
grasshopper optimization algorithm/particles warm optimization algorithm/pattern search/time complexity/statistical test/path planning分类
数理科学引用本文复制引用
肖怡心,刘三阳..融合模式搜索的蝗虫优化算法及其应用[J].西安电子科技大学学报（自然科学版）,2024,51(2):137-156,20.基金项目
国家自然科学基金(61877046,62106186,12271419) （61877046,62106186,12271419）
陕西省自然科学基础研究计划(2022JQ-620) （2022JQ-620）
中央高校基本科研基金(JB210701,XJS220709,QTZX23002) （JB210701,XJS220709,QTZX23002）
