Water cycle algorithm for solving constrained multi-objective optimization problems

Ali Sadollah, Hadi Eskandar, Joong Hoon Kim

Research output: Contribution to journalArticle

86 Citations (Scopus)

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 languageEnglish
Pages (from-to)279-298
Number of pages20
JournalApplied Soft Computing Journal
Volume27
DOIs
Publication statusPublished - 2014

Fingerprint

Constrained optimization
Multiobjective optimization
Water

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 journalArticle

@article{5274401f51dc4054ab4d2851b4e2d714,
title = "Water cycle algorithm for solving constrained multi-objective optimization problems",
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.",
keywords = "Benchmark function, Constrained optimization, Metaheuristics, Multi-objective optimization, Pareto optimal solutions, Water cycle algorithm",
author = "Ali Sadollah and Hadi Eskandar and Kim, {Joong Hoon}",
year = "2014",
doi = "10.1016/j.asoc.2014.10.042",
language = "English",
volume = "27",
pages = "279--298",
journal = "Applied Soft Computing",
issn = "1568-4946",
publisher = "Elsevier BV",

}

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 -