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 language | English |
|---|---|
| Pages (from-to) | 2587-2603 |
| Number of pages | 17 |
| Journal | Soft Computing |
| Volume | 19 |
| Issue number | 9 |
| DOIs | |
| Publication status | Published - 2015 Sep 17 |
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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 journal › Article
}
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
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U2 - 10.1007/s00500-014-1424-4
DO - 10.1007/s00500-014-1424-4
M3 - Article
VL - 19
SP - 2587
EP - 2603
JO - Soft Computing
JF - Soft Computing
SN - 1432-7643
IS - 9
ER -