A Comparison of Particle Swarm optimization and Global African Buffalo Optimization
The performance of Particle Swarm Optimization (PSO) brings attention to the field of algorithms when deals with different optimization problems. Due to her simple implementation, small consumption, and very effective in finding a solution in many problems, (PSO) becomes well known to the field of a...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IOP Publishing
2020
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/28804/1/A%20comparison%20of%20particle%20swarm%20optimization%20and%20global.pdf http://umpir.ump.edu.my/id/eprint/28804/ https://doi.org/10.1088/1757-899X/769/1/012034 |
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| Summary: | The performance of Particle Swarm Optimization (PSO) brings attention to the field of algorithms when deals with different optimization problems. Due to her simple implementation, small consumption, and very effective in finding a solution in many problems, (PSO) becomes well known to the field of algorithms. In addition, the late proposed algorithms mostly are compared to the well-known algorithm such as PSO. Thus, the Global African Buffalo Optimization (GABO) was proposed lately and yet not been compared to the old well-known algorithms in terms of accuracy and time consumption. However, in this paper, a comparison between Particle Swarm Optimization (PSO) and Global African Buffalo Optimization (GABO) algorithms was performed. Five different nonlinear equations with their upper and lower boundaries values were selected as the test optimization functions problem in addition to PSO was applied to real case study. The experimental results illustrated the differences in the performances of both algorithms toward the optimum solution. At the end of the experiments, the PSO algorithm quickly convergence towards the optimum solution using a few particles and iterations rather than GABO. However, the experimental result showed that PSO achieved good results in all the test cases within a short time. In many cases, PSO and GABO are promising optimization methods. |
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