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.
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF ) grant funded by the Korean government (MSIP) ( NRF-2013R1A2A1A01013886 ).
© 2014 Elsevier B.V. All rights reserved.
- Benchmark function
- Constrained optimization
- Multi-objective optimization
- Pareto optimal solutions
- Water cycle algorithm
ASJC Scopus subject areas