摘要
研究了基于模糊偏好的多目标粒子群算法,算法将种群的最优解集进行Pareto排序,并动态更新Pareto解集,使其更快速的靠近Pareto前沿,对非劣解进行模糊评价,根据目标偏好的模糊信息,来确定折衷解的满意解。经典算例验证,该算法在计算时间及非劣解质量上,要优于多目标遗传算法。
In this paper,a fuzzy multi-objective Particle Swarm Optimization(MOPSO) based on Pareto dominance hybrid algorithm is investigated.Pareto dominance and fuzzy decision making are incorporated into particle swarm optimization.The algorithm takes Pareto set as repository of particles that is later used by other particles to guide their own flight.Satisfactory solution is get from Pareto set through fuzzy evaluate.From theoretical computation,the validity and reliability of proposed algorithm are verified by test functions studied.Comparing with multi GA,a new algorithm has more short computation time and better pareto solution.
出处
《机电工程技术》
2009年第7期81-84,共4页
Mechanical & Electrical Engineering Technology
基金
广东省育苗工程基金(编号:LYM08098)
关键词
多目标粒子群
模糊
非劣解
multi-objective particle swarm optimization
fuzzy
pareto solution
