Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 279-298 |
| Number of pages | 20 |
| Journal | Applied Soft Computing Journal |
| Volume | 27 |
| DOIs | |
| Publication status | Published - 2014 |
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Keywords
- Benchmark function
- Constrained optimization
- Metaheuristics
- Multi-objective optimization
- Pareto optimal solutions
- Water cycle algorithm
ASJC Scopus subject areas
- Software
Cite this
Water cycle algorithm for solving constrained multi-objective optimization problems. / Sadollah, Ali; Eskandar, Hadi; Kim, Joong Hoon.
In: Applied Soft Computing Journal, Vol. 27, 2014, p. 279-298.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Water cycle algorithm for solving constrained multi-objective optimization problems
AU - Sadollah, Ali
AU - Eskandar, Hadi
AU - Kim, Joong Hoon
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Benchmark function
KW - Constrained optimization
KW - Metaheuristics
KW - Multi-objective optimization
KW - Pareto optimal solutions
KW - Water cycle algorithm
UR - http://www.scopus.com/inward/record.url?scp=84917740857&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84917740857&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2014.10.042
DO - 10.1016/j.asoc.2014.10.042
M3 - Article
VL - 27
SP - 279
EP - 298
JO - Applied Soft Computing
JF - Applied Soft Computing
SN - 1568-4946
ER -