An improved grey wolf with whale algorithm for optimization functions
The Grey Wolf Optimization (GWO) is a nature-inspired, meta-heuristic search optimization algorithm. It follows the social hierarchical structure of a wolf pack and their ability to hunt in packs. Since its inception in 2014, GWO is able to successfully solve several optimization problems and has sh...
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| Format: | Thesis |
| Language: | English English English |
| Published: |
2022
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| Online Access: | http://eprints.uthm.edu.my/8263/1/24p%20HAFIZ%20MAAZ%20ASGHER.pdf http://eprints.uthm.edu.my/8263/2/HAFIZ%20MAAZ%20ASGHER%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/8263/3/HAFIZ%20MAAZ%20ASGHER%20WATERMARK.pdf |
| Summary: | The Grey Wolf Optimization (GWO) is a nature-inspired, meta-heuristic search optimization algorithm. It follows the social hierarchical structure of a wolf pack and their ability to hunt in packs. Since its inception in 2014, GWO is able to successfully solve several optimization problems and has shown better convergence than the Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), and Evolutionary Programming (EP). Despite providing successful solutions to optimization problems, GWO has an inherent problem of poor exploration capability. The position-update equation in GWO mostly relies on the information provided by the previous solutions to generate new candidate solutions which result in poor exploration activity. Therefore, to overcome the problem of poor exploration in the GWO the exploration part of the Whale optimization algorithm (WOA) is integrated in it. The resultant Grey Wolf Whale Optimization Algorithm (GWWOA) offers better exploration ability and is able to solve the optimization problems to find the most optimal solution in search space. The performance of the proposed algorithm is tested and evaluated on five benchmarked unimodal and five multimodal functions. The simulation results show that the proposed GWWOA is able to find a fine balance between exploration and exploitation capabilities during convergence to global minima as compared to the standard GWO and WOA algorithms. |
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