摘要
粒子群优化算法的思想来源于人工生命和进化计算理论,由于其容易理解、易于实现,在很多领域得到了应用。由于传统的粒子群优化算法无法对多目标优化问题进行求解,因此文中利用模糊理论中的隶属度函数和给定的最优解评估选取原则,提出了一种适合求解约束型多目标优化问题的模糊粒子群算法(FPSO)。模糊粒子群算法很好地解决了汽车零部件可靠性稳健优化设计的求解问题,仿真结果证明,该算法可行而有效,同时也拓展了粒子群算法的应用领域。
Particle Swarm Optimization （PSO） is a new optimization technique originating from artificial life and evolutionary computation. PSO is applied to many fields for it is easily to be understood and performed. According to the evaluating principle of given optimal solution and the membership function of fuzzy theory, a new method named fuzzy particle swarm optimization （FPSO） is presented for solving MOP problem because the traditional PSO cannot solve the multi - objective optimization （MOP） problem. It is amenable to MOP problem with constraint model. FPSO can effectively cope with robust optimization design for reliability of automobile component. And it is proved to be effective and available by experiments. It also expands the application field of MOP arithmetic.
出处
《计算机仿真》
CSCD
2007年第2期153-156,共4页
Computer Simulation
关键词
模糊粒子群算法
隶属度函数
多目标优化
可靠性稳健优化设计
Fuzzy particle swarm optimization
Membership function
Multi - objective optimization
Robust optimization design for reliability
