Water cycle algorithm for solving multi-objective optimization problems

Ali Sadollah, Hadi Eskandar, Ardeshir Bahreininejad, Joong Hoon Kim

Research output: Contribution to journalArticle

40 Citations (Scopus)

Abstract

In this paper, the water cycle algorithm (WCA), a recently developed metaheuristic method is proposed for solving multi-objective optimization problems (MOPs). The fundamental concept of the WCA is inspired by the observation of water cycle process, and movement of rivers and streams to the sea in the real world. Several benchmark functions have been used to evaluate the performance of the WCA optimizer for the MOPs. The obtained optimization results based on the considered test functions and comparisons with other well-known methods illustrate and clarify the robustness and efficiency of the WCA and its exploratory capability for solving the MOPs.

Original languageEnglish
Pages (from-to)2587-2603
Number of pages17
JournalSoft Computing
Volume19
Issue number9
DOIs
Publication statusPublished - 2015 Sep 17

Fingerprint

Multiobjective Optimization Problems
Multiobjective optimization
Water
Cycle
Test function
Metaheuristics
Rivers
Benchmark
Robustness
Optimization
Evaluate

Keywords

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

ASJC Scopus subject areas

  • Software
  • Geometry and Topology
  • Theoretical Computer Science

Cite this

Water cycle algorithm for solving multi-objective optimization problems. / Sadollah, Ali; Eskandar, Hadi; Bahreininejad, Ardeshir; Kim, Joong Hoon.

In: Soft Computing, Vol. 19, No. 9, 17.09.2015, p. 2587-2603.

Research output: Contribution to journalArticle

Sadollah, Ali ; Eskandar, Hadi ; Bahreininejad, Ardeshir ; Kim, Joong Hoon. / Water cycle algorithm for solving multi-objective optimization problems. In: Soft Computing. 2015 ; Vol. 19, No. 9. pp. 2587-2603.
@article{f282463405d14eeb9c2b32bef8e81607,
title = "Water cycle algorithm for solving multi-objective optimization problems",
abstract = "In this paper, the water cycle algorithm (WCA), a recently developed metaheuristic method is proposed for solving multi-objective optimization problems (MOPs). The fundamental concept of the WCA is inspired by the observation of water cycle process, and movement of rivers and streams to the sea in the real world. Several benchmark functions have been used to evaluate the performance of the WCA optimizer for the MOPs. The obtained optimization results based on the considered test functions and comparisons with other well-known methods illustrate and clarify the robustness and efficiency of the WCA and its exploratory capability for solving the MOPs.",
keywords = "Benchmark function, Metaheuristics, Multi-objective optimization, Pareto-optimal solutions, Water cycle algorithm",
author = "Ali Sadollah and Hadi Eskandar and Ardeshir Bahreininejad and Kim, {Joong Hoon}",
year = "2015",
month = "9",
day = "17",
doi = "10.1007/s00500-014-1424-4",
language = "English",
volume = "19",
pages = "2587--2603",
journal = "Soft Computing",
issn = "1432-7643",
publisher = "Springer Verlag",
number = "9",

}

TY - JOUR

T1 - Water cycle algorithm for solving multi-objective optimization problems

AU - Sadollah, Ali

AU - Eskandar, Hadi

AU - Bahreininejad, Ardeshir

AU - Kim, Joong Hoon

PY - 2015/9/17

Y1 - 2015/9/17

N2 - In this paper, the water cycle algorithm (WCA), a recently developed metaheuristic method is proposed for solving multi-objective optimization problems (MOPs). The fundamental concept of the WCA is inspired by the observation of water cycle process, and movement of rivers and streams to the sea in the real world. Several benchmark functions have been used to evaluate the performance of the WCA optimizer for the MOPs. The obtained optimization results based on the considered test functions and comparisons with other well-known methods illustrate and clarify the robustness and efficiency of the WCA and its exploratory capability for solving the MOPs.

AB - In this paper, the water cycle algorithm (WCA), a recently developed metaheuristic method is proposed for solving multi-objective optimization problems (MOPs). The fundamental concept of the WCA is inspired by the observation of water cycle process, and movement of rivers and streams to the sea in the real world. Several benchmark functions have been used to evaluate the performance of the WCA optimizer for the MOPs. The obtained optimization results based on the considered test functions and comparisons with other well-known methods illustrate and clarify the robustness and efficiency of the WCA and its exploratory capability for solving the MOPs.

KW - Benchmark function

KW - Metaheuristics

KW - Multi-objective optimization

KW - Pareto-optimal solutions

KW - Water cycle algorithm

UR - http://www.scopus.com/inward/record.url?scp=84939165967&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84939165967&partnerID=8YFLogxK

U2 - 10.1007/s00500-014-1424-4

DO - 10.1007/s00500-014-1424-4

M3 - Article

AN - SCOPUS:84939165967

VL - 19

SP - 2587

EP - 2603

JO - Soft Computing

JF - Soft Computing

SN - 1432-7643

IS - 9

ER -