Parameter Investigation and Analysis for Elite Opposition Bacterial Foraging Optimization Algorithm
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Date
2019-04-22
Journal Title
Journal ISSN
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Publisher
Federal University of Technology Minna
Abstract
The investigation and analysis of algorithm parameters is an important task in most of the global optimization techniques. However, finding the best set of parameter value for the optimum performance of an algorithm still remain a challenging task in a modified Bacteria Foraging Optimization Algorithm (BFOA) since most toe the existing research focuses on the application o the algorithm and likewise it benchmarking with the global test function. The Elite Opposition Bacterial Foraging Optimization Algorithm (EOBFOA) is a modified nature inspired optimization algorithm from BFOA which focuses on the generation of an elite solution from the opposition solution for an optimization process. This research is focuses on the investigation of such parameters population size, probability of elimination dispersal, step size and number of chemotaxis so as to determine the extent to which they affect the optimal solution from the EOBFOA with respect to global minimum or least minimum standard deviation. From the results obtained, it was observed that the global minimum in EOBFOA depend on the exploitation ability of the bacteria in the search space.
Description
This paper explores the influence of key algorithm parameters on the performance of the Elite Opposition Bacterial Foraging Optimization Algorithm (EOBFOA), a modified version of the Bacteria Foraging Optimization Algorithm (BFOA) that integrates elite opposition-based learning to enhance solution quality. While most prior studies emphasize the application and benchmarking of BFOA, this research specifically investigates how parameters such as population size, elimination-dispersal probability, step size, and number of chemotaxis steps affect the algorithm's ability to reach the global minimum or achieve minimal standard deviation. The findings reveal that the algorithm's performance, particularly its convergence to the global minimum, is closely tied to the bacteria's exploitation capability within the search space.
Keywords
BFOA, EBFOA, elite solution, opposition solution, parameters
Citation
Maliki, D., Muazu, M. B., Kolo, J.G., & Olaniyi, O. M. (2019). Parameter Investigation and Analysis for Elite Opposition Bacterial Foraging Optimization Algorithm. Proceedings of the 3rd International Engineering Conference (IEC 2019). Federal University of Technology Minna, Nigeria. Pp 464-471.