现代信息科技2026，Vol.10Issue(15)：71-76,6.DOI:10.19850/j.cnki.2096-4706.2026.15.014
一种双精英引导的群体智能优化算法
A Dual-Elite-Guided Swarm Intelligence Optimization Algorithm
摘要
Abstract
To address the unstable search and premature convergence of existing swarm intelligence algorithms in continuous optimization,as well as the insufficient robustness of single-elite guidance in complex multimodal environments,this paper proposes a Dual-Elite-Guided Swarm Intelligence Optimization(DEGSIO)algorithm.The algorithm simultaneously maintains the historical best and second-best individuals,designs an individual update rule based on their collaborative guidance,and introduces a linearly decreasing random control parameter to balance global exploration and local exploitation.The algorithm is compared with HHO,PSO,GWO,WOA,and DE on eight benchmark functions,and the Friedman test is employed.The experimental results show that DEGSIO effectively avoids premature convergence on multiple functions,is competitive in terms of accuracy,convergence speed,and stability,and achieves overall performance statistically comparable to that of HHO,which verifies the effectiveness of the dual-elite guidance strategy.关键词
群体智能优化/双精英策略/个体更新规则/连续优化Key words
swarm intelligence optimization/dual-elite strategy/individual update rule/continuous optimization分类
信息技术与安全科学引用本文复制引用
郑水飞,聂斌,章祉瑶,李想..一种双精英引导的群体智能优化算法[J].现代信息科技,2026,10(15):71-76,6.基金项目
国家自然科学基金(82260849) （82260849）
江西中医药大学校级科技创新团队发展计划(CXTD22015) （CXTD22015）
