Water cycle algorithm for solving constrained multi-objective optimization problems

Ali Sadollah, Hadi Eskandar, Joong Hoon Kim

Research output: Contribution to journalArticlepeer-review

218 Citations (Scopus)


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
Publication statusPublished - 2015 Feb

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF ) grant funded by the Korean government (MSIP) ( NRF-2013R1A2A1A01013886 ).

Publisher Copyright:
© 2014 Elsevier B.V. All rights reserved.


  • Benchmark function
  • Constrained optimization
  • Metaheuristics
  • Multi-objective optimization
  • Pareto optimal solutions
  • Water cycle algorithm

ASJC Scopus subject areas

  • Software


Dive into the research topics of 'Water cycle algorithm for solving constrained multi-objective optimization problems'. Together they form a unique fingerprint.

Cite this