成都大学学报（自然科学版）2024，Vol.43Issue(4)：371-378,8.DOI:10.3969/j.issn.1004-5422.2024.04.006
动态非线性参数的反向学习黏菌算法
Opposition-Based Learning Slime Mold Algorithm of Dynamic Nonlinear Parameters
摘要
Abstract
In response to the low convergence accuracy,propensity for local optima,and slow convergence speed of the slime mold algorithm,a dynamic nonlinear parameter opposition-based learning slime mold algorithm is proposed.By using a opposition-based learning strategy to enrich population diversity and ob-tain a better initial population,the algorithm's optimization performance and convergence speed are im-proved.A dynamic nonlinear decreasing strategy is introduced to dynamically adjust the slime mold search area,to coordinate global exploration and local development to enhance the algorithm's ability to avoid lo-cal optima and to improve convergence accuracy.Experimental comparisons between different algorithms are conducted by using several benchmark test functions.The results show that the improved algorithm has stronger optimization characteristics and faster convergence speed,with varying degrees of improvement in convergence accuracy.Finally,the reliability and effectiveness of the improved algorithm in practical ap-plication problems are validated through two engineering design problems.关键词
反向学习/动态非线性递减/黏菌算法/算法优化Key words
opposition-based learning/dynamic nonlinear decreasing/slime mold algorithm/algorithm op-timization分类
信息技术与安全科学引用本文复制引用
饶爽,曲良东,梅雨琳..动态非线性参数的反向学习黏菌算法[J].成都大学学报（自然科学版）,2024,43(4):371-378,8.基金项目
广西自然科学基金项目(2023GXNSFBA026019、2019GXNSFAA185033) （2023GXNSFBA026019、2019GXNSFAA185033）
广西科技基地和人才专项(GUIKEAD18126010、GUIKEAD22080021) （GUIKEAD18126010、GUIKEAD22080021）
