计算机工程与应用2025，Vol.61Issue(23)：90-109,20.DOI:10.3778/j.issn.1002-8331.2504-0270
自组织映射更新的双种群约束多目标狼群算法
Dual-Population Constrained Multi-Objective Wolf Pack Algorithm with Self-Organizing Map-ping Update
摘要
Abstract
To address the shortcomings of conventional multi-objective wolf pack algorithms,including their inability to handle constraints,premature convergence caused by population clustering,and outdated update mechanisms leading to loss of high-quality solutions,this paper proposes a self-organizing map updated dual-population constrained multi-objective wolf pack algorithm(CMOWPA-S).The algorithm constructs a dual-population structure where the main popu-lation employs constrained dominance principles to ensure operation within feasible regions,while the auxiliary popula-tion disregards constraints to enhance solution quality discovery.A dual-optimization hunting strategy is introduced:elite wolves assist the leader in summoning the pack during the raid phase,and Lévy flight strategy is incorporated in the siege phase to improve local optimum escape capability.A self-organizing map-based population update mechanism is designed to extract neighborhood information for generating superior offspring,ensuring the inheritance of high-quality solutions.Environmental selection strategies are implemented to eliminate redundant populations.To verify the performance of the algorithm,it is compared with 4 classic and 5 emerging constrained multi-objective optimization algorithms on 14 simulated constrained multi-objective problems,and with 5 new constrained multi-objective optimization algorithms on 10 real constrained multi-objective problems.The experimental results show that CMOWPA-S can effectively solve constrained objective optimization problems,avoid falling into local optima,and obtain solutions with good population diversity.关键词
狼群算法/约束优化/多目标优化/双种群/自组织映射Key words
wolf pack algorithm/constrained optimization/multi-objective optimization/dual-population/self-organizing map分类
信息技术与安全科学引用本文复制引用
康水平,唐光清,樊棠怀,王晖,吕莉..自组织映射更新的双种群约束多目标狼群算法[J].计算机工程与应用,2025,61(23):90-109,20.基金项目
江西省教育厅科技项目(GJJ201915) （GJJ201915）
国家自然科学基金(62463021). （62463021）
