Application of Mutation Operators on Grey Wolf Optimizer
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2021, 00, pp. 1-6
- Issue Date:
- 2021-11-13
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Application_of_Mutation_Operators_on_Grey_Wolf_Optimizer.pdf | Published version | 1.76 MB |
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Nature-inspired computing has been widely used for solving different optimization problems. Grey wolf optimization (GWO) is one of the recent addition in the category of swarm-based techniques. The swarm-based algorithms try to find a balance between exploitation and exploration through different steps/processes. In this work, mutation operation is performed on the GWO algorithm to create exploration and avoid local stagnation. A comparative analysis has been performed to evaluate the effect of various mutation operators on GWO. The algorithms are tested on ten well-known unimodal and multimodal benchmark functions. Unimodal functions evaluate the exploitation property, whereas multimodal functions evaluate the exploration property of the algorithms. The practical performance of these algorithms is shown on three different CEC2011 real-world problems. The results show that the mutation operator generate improved or comparable results in different optimization problems.
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