A novel strategy of pareto-optimal solution searching in multi-objective particle swarm optimization (MOPSO)

被引：53
|
作者：
Yang, Junjie [1 ]
Zhou, Jianzhong [1 ]
Liu, Li [1 ]
Li, Yinghai [1 ]
机构：
[1] Huazhong Univ Sci & Technol, Sch Hydropower & Informat Engn, Wuhan 430074, Peoples R China
来源：
COMPUTERS & MATHEMATICS WITH APPLICATIONS | 2009年 / 57卷 / 11-12期
基金：
美国国家科学基金会;
关键词：
Evolutionary algorithms; Multiple objectives; Particle swarm optimization; Optimal regulation of cascade reservoirs;
D O I：
10.1016/j.camwa.2008.10.009
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In multi-objective particle swarm optimization (MOPSO) algorithms, finding the global optimal particle (gBest) for each particle of the swarm from a set of non-dominated solutions is very difficult yet an important problem for attaining convergence and diversity of solutions. First, a new Pareto-optimal solution searching algorithm for finding the gBest in MOPSO is introduced in this paper, which can compromise global and local searching based on the process of evolution. The algorithm is implemented and is compared with another algorithm which uses the Sigma method for finding gBest on a set of well-designed test functions. Finally, the multi-objective optimal regulation of cascade reservoirs is successfully solved by the proposed algorithm. Crown Copyright (c) 2008 Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：1995 / 2000
页数：6
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