October 2017
Intermediate to advanced
304 pages
8h 3m
English
book
Meta-heuristic and Evolutionary Algorithms for Engineering Optimization
by Omid Bozorg-Haddad, Mohammad Solgi, Hugo A. Loáiciga
Content preview from Meta-heuristic and Evolutionary Algorithms for Engineering Optimization
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20 Water Cycle Algorithm
Summary
This chapter describes the water cycle algorithm (WCA), which is a relatively new meta‐heuristic optimization algorithm. The fundamental concepts of the WCA are inspired by natural phenomena concerning the water cycle and how rivers and streams flow to the sea. The next sections present the background and applications of the WCA, explain the WCA, and provide a pseudocode.
20.1 Introduction
The water cycle algorithm (WCA) was introduced by Eskandar et al. (2012). The authors compared the results of the WCA with those of other meta‐heuristic algorithms such as the genetic algorithm (GA), particle swarm optimization (PSO) algorithm, harmony search (HS), bee colony, and differential evolution (DE). Their results showed that the WCA is a suitable method for solving constrained optimization problems and competes favorably with other meta‐heuristic algorithms. Eskandar et al. (2013) illustrated the application of the WCA by solving the problem of designing truss structures and compared the results with those of other meta‐heuristic algorithms such as the GA, PSO, mine blast algorithm (MBA), etc. The results of their comparison demonstrated the strong capability of the WCA algorithm to find optimal solutions and its rapid convergence. Bozorg‐Haddad et al. (2014) applied the WCA to find optimal operation strategies for a four‐reservoir system in Iran. The results demonstrated the high efficiency and reliability of the WCA in solving reservoir operation ...
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