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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Published in:Computers & operations research Vol. 55; pp. 1 - 11
Main Author: Zheng, Yu-Jun
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.03.2015
Pergamon Press Inc
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ISSN:0305-0548, 1873-765X, 0305-0548
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Abstract 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.
AbstractList 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.
Author Zheng, Yu-Jun
Author_xml – sequence: 1
  givenname: Yu-Jun
  orcidid: 0000-0002-6095-6325
  surname: Zheng
  fullname: Zheng, Yu-Jun
  email: yujun.zheng@computer.org
  organization: College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310023, China
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Keywords Water wave optimization (WWO)
Wave-current-bottom interactions
High-speed train scheduling
Metaheuristic method
Optimization
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Snippet 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...
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SubjectTerms 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
Title Water wave optimization: A new nature-inspired metaheuristic
URI https://dx.doi.org/10.1016/j.cor.2014.10.008
https://www.proquest.com/docview/1638710028
https://www.proquest.com/docview/1786190884
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