A novel multi-objective orthogonal simulated annealing algorithm for solving multi-objective optimization problems with a large number of parameters

被引：0
|
作者：
Shu, LS [1 ]
Ho, SJ
Ho, SY
Chen, JH
Hung, MH
机构：
[1] Feng China Univ, Dept Informat Engn & Comp Sci, Taichung 407, Taiwan
[2] Natl Huwei Inst Technol, Dept Automat Engn, Huwei 632, Taiwan
来源：
GENETIC AND EVOLUTIONARY COMPUTATION - GECCO 2004, PT 1, PROCEEDINGS | 2004年 / 3102卷
关键词：
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this paper, a novel multi-objective orthogonal simulated annealing algorithm MOOSA using a generalized Pareto-based scale-independent fitness function and multi-objective intelligent generation mechanism (MOIGM) is proposed to efficiently solve multi-objective optimization problems with large parameters. Instead of generate-and-test methods, MOIGM makes use of a systematic reasoning ability of orthogonal experimental design to efficiently search for a set of Pareto solutions. It is shown empirically that MOOSA is comparable to some existing population-based algorithms in solving some multi-objective test functions with a large number of parameters.
引用
收藏
页码：737 / 747
页数：11
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