Water wave optimization: A new nature-inspired metaheuristic

Nature-inspired computing has been a hot topic in scientific and engineering fields in recent years. Inspired by the shallow water wave theory, the paper presents a novel metaheuristic method, named water wave optimization (WWO), for global optimization problems. We show how the beautiful phenomena...

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Vydáno v:Computers & operations research Ročník 55; s. 1 - 11
Hlavní autor: Zheng, Yu-Jun
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Ltd 01.03.2015
Pergamon Press Inc
Témata:
Algorithms
Computation
Heuristic
Heuristic methods
High speed rail
High-speed train scheduling
Metaheuristic method
Operations research
Optimization
Optimization algorithms
Scheduling algorithms
Searching
Studies
Trains
Water wave optimization (WWO)
Water waves
Wave propagation
Wave-current-bottom interactions
China
Water wave optimization (WWO)
Wave-current-bottom interactions
High-speed train scheduling
Metaheuristic method
Optimization
ISSN:0305-0548, 1873-765X, 0305-0548
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Shrnutí:Nature-inspired computing has been a hot topic in scientific and engineering fields in recent years. Inspired by the shallow water wave theory, the paper presents a novel metaheuristic method, named water wave optimization (WWO), for global optimization problems. We show how the beautiful phenomena of water waves, such as propagation, refraction, and breaking, can be used to derive effective mechanisms for searching in a high-dimensional solution space. In general, the algorithmic framework of WWO is simple, and easy to implement with a small-size population and only a few control parameters. We have tested WWO on a diverse set of benchmark problems, and applied WWO to a real-world high-speed train scheduling problem in China. The computational results demonstrate that WWO is very competitive with state-of-the-art evolutionary algorithms including invasive weed optimization (IWO), biogeography-based optimization (BBO), bat algorithm (BA), etc. The new metaheuristic is expected to have wide applications in real-world engineering optimization problems.
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2014.10.008