计算机应用研究2011，Vol.28Issue(7)：2476-2478,3.DOI:10.3969/j.issn.1001-3695.2011.07.020
求解约束优化问题的动态邻域粒子群算法
Dynamic neighborhood particle swarm optimization for constrained optimization problems
摘要
Abstract
Particle swarm optimization (PSO) for solving constrained optimization problems existed serious premature conver-gence , in order to inhibit this phenomenon, this paper proposed an improved constraint adaptive and dynamic neighborhood particle swarm optimization (IPSO). Algorithm used dynamic neighborhood strategy to improve the global search capability, and designed an improved adaptive constraint handling method. According to iteration number linear increase searched biases coefficient, in the early bias tended to search feasible solution, while in the latter tended to search the optimal solution, and adopted sequential quadratic programming to enhance local search capabilities. Through the experimental comparison of bench-mark function shows that the algorithm for constrained optimization problems with better global convergence.关键词
粒子群优化/动态邻域/约束优化:序列二次规划Key words
particle swarm optimization/ dynamic neighborhood/ constrained optimization/ sequential quadratic program-ming分类
信息技术与安全科学引用本文复制引用
彭虎,田俊峰,邓长寿..求解约束优化问题的动态邻域粒子群算法[J].计算机应用研究,2011,28(7):2476-2478,3.基金项目
江西省教育厅科技基金资助项目(GJJI0616) （GJJI0616）
