摘要
针对实践中多目标优化问题(MOPs)的Pareto解集(PS)未知且比较复杂的特性,提出了一种基于'探测'(Exploration)与'开采'(Exploitation)的多目标进化算法(MOEA)——MOEA/2E。该算法在进化过程中采用'探测'与'开采'相结合的方法,用进化操作不断地探测新的搜索区域,用局部搜索充分开采优秀的解区域,并用隐最优个体保留机制保存每一代的最优个体。与目前最流行且有效的多目标进化算法NSGA-Ⅱ及SPEA-Ⅱ进行的比较实验结果表明,MOEA/2E获得的Pareto最优解集具有更好的收敛性与分布性。
In view of the fact that Pareto Set (PS) of multi-objective optimization problems (MOPs) is often unknown and complex in practice. This paper proposes a multi-objective evolutionary algorithm (MOEA) based on "Exploration" and "Exploitation", named MOEA/2E. This algorithm combines "Exploration" and "Exploitation" in the evolutionary process. It explores new searching areas with evolutionary operators, exploits promising areas effectively with local search and stores optimal individual of a population with elitism. Compared with two popular and efficient MOEAs--NSGA-Ⅱ and SPEA- Ⅱ, the experimental results demonstrate that MOEA/2E can obtain Pareto optimal solutions set with better convergence and diversity.
出处
《高技术通讯》
EI
CAS
CSCD
北大核心
2010年第2期143-149,共7页
Chinese High Technology Letters
基金
国家自然科学基金(60773047)
863计划(2001AA114060)
湖南省教育厅重点科研项目(06A074)
湖南省研究生科研创新项目(x2008yjscx18)资助项目