Water cycle algorithm for solving constrained multi-objective optimization problems

被引：217
|
作者：
Sadollah, Ali [1 ]
Eskandar, Hadi [2 ]
Kim, Joong Hoon [1 ]
机构：
[1] Korea Univ, Sch Civil Environm & Architectural Engn, Seoul 136713, South Korea
[2] Univ Semnan, Fac Mech Engn, Semnan, Iran
来源：
APPLIED SOFT COMPUTING | 2015年 / 27卷
基金：
新加坡国家研究基金会;
关键词：
Multi-objective optimization; Water cycle algorithm; Pareto optimal solutions; Benchmark function; Metaheuristics; Constrained optimization; PARTICLE SWARM OPTIMIZATION; GENETIC ALGORITHM;
D O I：
10.1016/j.asoc.2014.10.042
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a metaheuristic optimizer, the multi-objective water cycle algorithm (MOWCA), is presented for solving constrained multi-objective problems. The MOWCA is based on emulation of the water cycle process in nature. In this study, a set of non-dominated solutions obtained by the proposed algorithm is kept in an archive to be used to display the exploratory capability of the MOWCA as compared to other efficient methods in the literature. Moreover, to make a comprehensive assessment about the robustness and efficiency of the proposed algorithm, the obtained optimization results are also compared with other widely used optimizers for constrained and engineering design problems. The comparisons are carried out using tabular, descriptive, and graphical presentations. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码：279 / 298
页数：20
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