计算机技术与发展2024，Vol.34Issue(9)：182-187,6.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0173
求解全局优化问题的SCA-VPPSO算法及其应用
A Novel SCA-VPPSO Algorithm for Global Optimization Problems and Its Engineering Application
摘要
Abstract
The sine cosine algorithm and the velocity paused particle swarm optimization algorithm are two highly effective metaheuristic algorithms employed for addressing continuous global optimization problems.However,when applied to practical scenarios,these algorithms consistently encounter the challenge of escaping local minima.Therefore,we propose the SCA-VPPSO algorithm,a novel hybrid search algorithm designed for addressing continuous global optimization problems.Based on the search framework of velocity paused particle swarm algorithm,the sine-cosine search operator is transformed from the original full-dimensional update strategy to a partial dimension update strategy,which is used in development and exploration,and integrates with the local search behavior of velocity paused particle swarm algorithm to form a two-mode local exploration mode.The hybrid SCA-VPPSO algorithm can balance local utilization and global exploration more effectively,thus enhancing the ability of the algorithm to escape the local minimum and obtain better results.The performance evaluation of the proposed algorithm,in conjunction with the sine cosine algorithm,velocity paused particle swarm optimization algorithm,and two recently published exemplary algorithms,was carried out on both the CEC2019 test set and an engineering practical application.The findings demonstrated a notable enhancement in the optimization performance of the proposed algorithm,thereby broadening its potential applications and presenting a novel hybrid search approach for the advancement of metaheuristic algorithms.关键词
全局优化/粒子群算法/正余弦算法/元启发式算法/工程应用Key words
global optimization/particle swarm optimization(PSO)/sine cosine algorithm(SCA)/metaheuristic algorithm/engineering application分类
信息技术与安全科学引用本文复制引用
曹琦,程雷平,徐成,方宁..求解全局优化问题的SCA-VPPSO算法及其应用[J].计算机技术与发展,2024,34(9):182-187,6.基金项目
国家自然科学基金(61871010) （61871010）
