计算机工程与应用2017，Vol.53Issue(20)：31-37,60,8.DOI:10.3778/j.issn.1002-8331.1606-0137
动态邻居维度学习的多目标粒子群算法
Multi-objective particle swarm optimization based on dynamic neighborhood for dimensional learning
摘要
Abstract
Focus on the poor behavior of the diversity for multi-objective particle swarm optimization and the selection pressure of population increasing with the variable dimension,a Multi-Objective Particle Swarm Optimization based on Dynamic Neighborhood of Dimensional Learning(DNDL-MOPSO)is proposed.Firstly,an optimum dimensional indi-vidual is established.Then based on the individual and social knowledge,the proposed algorithm improves the formula of the velocity updating and uses a strategy that each dimensional learning object is not fixed. Finally, the random guide learning strategy is used to alleviate the selection pressure.The experimental results indicate that the new algorithm can improve the global convergence and increase the diversity of population.It is effective to solve the benchmark multimodal optimization problems.关键词
粒子群算法/多目标优化/动态邻居/最优维度粒子/随机向导学习Key words
Particle Swarm Optimization(PSO)/multi-objective optimization/dynamic neighbor/optimum dimensional individual/random guide learning分类
信息技术与安全科学引用本文复制引用
肖闪丽,王宇嘉,聂善坤..动态邻居维度学习的多目标粒子群算法[J].计算机工程与应用,2017,53(20):31-37,60,8.基金项目
国家自然科学基金(No.61403249) （No.61403249）
上海工程技术大学研究生科研创新项目(No.E309031601178). （No.E309031601178）
