Multi-objective optimization algorithm based on artificial physics optimization

被引：0
|
作者：
Wang, Yan [1 ,2 ]
Zeng, Jian-Chao [2 ]
机构：
[1] College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
[2] Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan 030024, China
来源：
Kongzhi yu Juece/Control and Decision | 2010年 / 25卷 / 07期
关键词：
Particle swarm optimization (PSO) - Pareto principle;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
A multi-objective optimization algorithm based on artificial physics optimization (MOAPO) is presented to solve multi-objective optimization problems. According to the trait of multi-objective problems, by drawing lessons from aggregating functions method, searching for Pareto optimal set of multi-objective optimization problems is implemented by using APO algorithm. The inertia weight and gravitation coefficient are dynamic changing to explore the search space more efficiently. The experimental simulations show that MOAPO is effective for multi-objective problems with a better diversity compared with NSGA-II algorithm and multi-objective optimization algorithms based on particle swarm optimization (PSO).
引用
收藏
页码：1040 / 1044
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