Water cycle algorithm with evaporation rate for solving constrained and unconstrained optimization problems

被引:165
作者
Sadollah, Ali [1 ]
Eskandar, Hadi [2 ]
Bahreininejad, Ardeshir [3 ]
Kim, Joong Hoon [1 ]
机构
[1] Korea Univ, Sch Civil Environm & Architectural Engn, Seoul 136713, South Korea
[2] Univ Semnan, Fac Engn, Semnan, Iran
[3] Inst Teknol Brunei, Fac Engn, BE-1410 Bandar Seri Begawan, Brunei
基金
新加坡国家研究基金会;
关键词
Water cycle algorithm; Metaheuristics; Global optimization; Unconstrained; Constrained; Benchmark functions; ENGINEERING OPTIMIZATION;
D O I
10.1016/j.asoc.2015.01.050
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents a modified version of the water cycle algorithm (WCA). The fundamental concepts and ideas which underlie the WCA are inspired based on the observation of water cycle process and how rivers and streams flow to the sea. New concept of evaporation rate for different rivers and streams is defined so called evaporation rate based WCA (ER-WCA), which offers improvement in search. Furthermore, the evaporation condition is also applied for streams that directly flow to sea based on the new approach. The ER-WCA shows a better balance between exploration and exploitation phases compared to the standard WCA. It is shown that the ER-WCA offers high potential in finding all global optima of multimodal and benchmark functions. The WCA and ER-WCA are tested using several multimodal benchmark functions and the obtained optimization results show that in most cases the ER-WCA converges to the global solution faster and offers more accurate results than the WCA and other considered optimizers. Based on the performance of ER-WCA on a number of well-known benchmark functions, the efficiency of the proposed method with respect to the number of function evaluations (computational effort) and accuracy of function value are represented. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:58 / 71
页数:14
相关论文
共 32 条
[11]  
Goldberg D. E., 1989, Genetic Algorithms in Search, Optimization, and Machine Learning
[12]  
Holland J., 1975, ADAPTATION NATURAL A, DOI DOI 10.7551/MITPRESS/1090.001.0001
[13]  
Jong D., 1975, ANAL BEHAV CLASS GEN
[14]  
Karaboga D., 2005, TECHNICAL REPORT
[15]  
Kennedy J, 1997, IEEE SYS MAN CYBERN, P4104, DOI 10.1109/ICSMC.1997.637339
[16]  
Kennedy J, 1995, 1995 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS PROCEEDINGS, VOLS 1-6, P1942, DOI 10.1109/icnn.1995.488968
[17]   OPTIMIZATION BY SIMULATED ANNEALING [J].
KIRKPATRICK, S ;
GELATT, CD ;
VECCHI, MP .
SCIENCE, 1983, 220 (4598) :671-680
[18]   A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice [J].
Lee, KS ;
Geem, ZW .
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2005, 194 (36-38) :3902-3933
[19]   Biomimicry of social foraging bacteria for distributed optimization: Models, principles, and emergent behaviors [J].
Liu, Y ;
Passino, KM .
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS, 2002, 115 (03) :603-628
[20]   Structural topology optimization using ant colony optimization algorithm [J].
Luh, Guan-Chun ;
Lin, Chun-Yi .
APPLIED SOFT COMPUTING, 2009, 9 (04) :1343-1353