基于微粒群的约束优化
onstrained Optimization via Particle Swarm Optimization Algorithm
微粒群算法在处理约束优化问题时，难以兼顾约束、优化之间的关系，针对这一问题提出了一种泛学习微粒群算法（ULPSO），该算法通过引入微粒不可行历史最优，使得微粒的学习更具多样性和有效性，增强了微粒群算法的搜索智能。通过对常用的13个基准函数的测试对比分析，表明了该算法求解约束优化问题的计算快速性，稳定性和有效性。
In dealing with constrained optimization problems，particle swarm optimization(PSO) is difficult to balance the relationship between constrains and optimization. A ubiquitous—learning swarm optimization (ULPSO) was proposed in order to solve this problem.Via incorporating infeasible personal best for each particle, this algorithm makes the study of particles more diversity and efficiency, enhances search intelligence.Finally,it is applied to a set of 13 well-known benchmark functions, and the experimental results illustrate its computing speed,stability and efficiency.
胡鹏、王猛
计算技术、计算机技术
微粒群约束优化不可行历史最优边界学习
particle swarm optimizationonstrained Optimizationinfeasible personal bestboundarylearn
胡鹏,王猛.基于微粒群的约束优化[EB/OL].(2009-04-01)[2025-08-03].http://www.paper.edu.cn/releasepaper/content/200904-26.点此复制
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