Selective Opposition based Grey Wolf Optimization
| Autor: | Souvik Dhargupta, Ram Sarkar, Seyedali Mirjalili, Manosij Ghosh |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Flexibility (engineering)
0209 industrial biotechnology Mathematical optimization Computer science media_common.quotation_subject General Engineering Opposition (politics) 02 engineering and technology Computer Science Applications 020901 industrial engineering & automation Rate of convergence Artificial Intelligence Robustness (computer science) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Simplicity Metaheuristic media_common |
| Zdroj: | Expert Systems with Applications. 151:113389 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2020.113389 |
| Popis: | The use of metaheuristics is widespread for optimization in both scientific and industrial problems due to several reasons, including flexibility, simplicity, and robustness. Grey Wolf Optimizer (GWO) is one of the most recent and popular algorithms in this area. In this work, opposition-based learning (OBL) is combined with GWO to enhance its exploratory behavior while maintaining a fast convergence rate. Spearman's correlation coefficient is used to determine the omega (ω) wolves (wolves with the lowest social status in the pack) on which to perform opposition learning. Instead of opposing all the dimensions in the wolf, a few dimensions of the wolf are selected on which opposition is applied. This assists with avoiding unnecessary exploration and achieving a fast convergence without deteriorating the probability of finding optimum solutions. The proposed algorithm is tested on 23 optimization functions. An extensive comparative study demonstrates the superiority of the proposed method. The source code for this algorithm is available at "https://github.com/dhargupta-souvik/sogwo" |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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