Water wave optimization: A new nature-inspired metaheuristic

By Yujun Zheng. Computers & Operations Research, 2015

540 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Industrial and Manufacturing Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/adb56e3adf2f9a67b742a25b07862acc907bd3cb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Kashif Hussain
2. Shi Cheng
3. M. N. Mohd Salleh
4. Marcin Wozniak
5. Ahmet Cosar
6. Dawid Połap
7. Tansel Dökeroğlu
8. Tayfun Kucukyilmaz

In Industrial and Manufacturing Engineering

1. Marcin Wozniak
2. Shi Cheng
3. Ahmet Cosar
4. Dawid Połap
5. Tansel Dökeroğlu
6. Tayfun Kucukyilmaz
7. M. N. Mohd Salleh
8. Kashif Hussain

In Ocean Engineering

1. Marcin Wozniak
2. Shi Cheng
3. Dawid Połap
4. Tansel Dökeroğlu
5. M. N. Mohd Salleh
6. Kashif Hussain
7. Yuhui Shi

Citations in the corpus

• Metaheuristic research: a comprehensive survey (2018, methodology): “…Optimization (Kaveh and Bakhshpoori 2016) WFA Water Flow-like Algorithm (Yang and Wang 2007) WPA Wolf Pack Algorithm (Wu and Zhang 2014) WS Warping Search (Gonçalves et al. 2008) WSA Weighted Superposition Attraction (Baykasoğlu and Akpinar 2015) WWO Water Wave Optimization (Zheng 2015)”
• A survey on new generation metaheuristic algorithms (2019, background): “Zheng (2015) proposes Water Wave Optimization (WWO), for global optimization problems.”
• Red fox optimization algorithm (2021, background): “BA CSA DA FA FPA MFO RFO WWO 𝑥1 57.53212 55.46532 51.65482 51.98541 54.36485 48.32162 52.1581 57.26512 𝑥2 19.75461 16.23612 17.65231 25.65891 23.65232 18.62531 23.01251 23.56412 𝑥3 19.32561 22.31521 22.63215 14.65912 18.65231 20.4121 16.1401 15.65423 𝑥4 44.65231 45.92531 51.02643 46.95641 56.124”