Multi-Objective Mean Particle Swarm Optimization Algorithm

被引：0

作者：

Pei, Shengyu ^{[1
]}

Zhou, Yongquan ^{[1
]}

机构：

[1] Guangxi Univ Nationalities, Coll Math & Comp Sci, Nanning, Guangxi, Peoples R China

来源：

2010 8TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA) | 2010年

关键词：

Particle swarm optimization; Mean particle swarm optimization; Multi-objective constrained optimization; Pareto non-dominated; Crowding distance;

D O I：

10.1109/WCICA.2010.5553900

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, Pareto non-dominated ranking, crowding distance, tournament selection methods and mean particle swarm optimization were introduced, we using these concepts, a novel mean particle swarm optimization algorithm for multi-objective optimization problem is proposed. Finally, three standard non-constrained multi-objective functions and four constrained multi-objective functions are used to test the performance of the algorithm. The experiment results show that the proposed approach is an efficient and feasible.

引用

页码：3315 / 3319

页数：5

共 9 条

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