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 |
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| Main Author: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
Elsevier Ltd
01.03.2015
Pergamon Press Inc |
| Subjects: | |
| ISSN: | 0305-0548, 1873-765X, 0305-0548 |
| Online Access: | Get full text |
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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. |
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| 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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| CODEN | CMORAP |
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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 |
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