期刊
APPLIED SOFT COMPUTING
卷 27, 期 -, 页码 279-298出版社
ELSEVIER
DOI: 10.1016/j.asoc.2014.10.042
关键词
Multi-objective optimization; Water cycle algorithm; Pareto optimal solutions; Benchmark function; Metaheuristics; Constrained optimization
资金
- National Research Foundation of Korea (NRF) grant - Korean government (MSIP) [NRF-2013R1A2A1A01013886]
- National Research Foundation of Korea [2013R1A2A1A01013886] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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.
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