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Water cycle algorithm for solving constrained multi-objective optimization problems
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DOI：10.1016/j.asoc.2014.10.042.png)
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Abstract
En 中文
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.
Keywords:
Multi-objective optimization
Water cycle algorithm
Pareto optimal solutions
Benchmark function
Metaheuristics
Constrained optimization
Journal
IF:
6.6
Papers:
1.4W
Citations:
4.8W
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