计算机技术与发展Issue(1)：87-90,95,5.DOI:10.3969/j.issn.1673-629X.2015.01.020
一种改进的粒子群多目标优化算法研究
Research on an Improved Multi-objective Optimization Algorithm of Particle Swarm
摘要
Abstract
To solve the problem that resource contention and conflict between the various solutions in multi-objective optimization pro-cessing,and can't be convergence duo to the precocious brought by convergence,introduce a multi-sub-population co-evolution mecha-nism to overcome these shortcomings. The algorithm has adopted different populations to optimize different targets. Meanwhile,it intro-duces an external archive and elite learning strategies,in this way it can obtain more solutions of external archive to choose. Elite learning strategies makes the algorithm has a better distribution and convergence. Finally,the algorithm is applied into the multi-objective test function,the experimental results show that the improved algorithm has a better convergence and distribution than NSGA II.关键词
多目标优化/粒子群算法/多子种群/外部档案Key words
multi-objective optimization/particle swarm algorithm/multi-sub-population/external archive分类
信息技术与安全科学引用本文复制引用
刘慧慧..一种改进的粒子群多目标优化算法研究[J].计算机技术与发展,2015,(1):87-90,95,5.基金项目
国家自然科学基金资助项目(61070234) （61070234）
