Enhanced convergence of Bat Algorithm based on dimensional and inertia weight factor

Ramli, Mohamad Raziff and Abal Abas, Zuraida and Desa, Mohammad Ishak and Zainal Abidin, Zaheera and Al Azzam, Malik Bader Hasan (2019) Enhanced convergence of Bat Algorithm based on dimensional and inertia weight factor. Journal of King Saud University - Computer and Information Sciences, 31 (4). pp. 452-458. ISSN 1319-1578

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Abstract

Heuristic optimisation method typically hinges on the efficiency in exploitation and global diverse exploration. Previous research has shown that Bat Algorithm could provide a good exploration and exploitation of a solution. However, Bat Algorithm can be get trapped in a local minimum in some multi-dimensional functions. Thus, the phenomenon of slow convergence rate and low accuracy still exits. This paper aims to modify the exploitation of Bat Algorithm in optimising the solution by modifying dimensional size and providing inertia weight. Benchmark test function is then performed for the basic Bat Algorithm and the modified Bat Algorithm (MBA) for comparison. The result is analysed according to the number of iteration needed for a convergence toward the objective. From simulations, it is found that the modified dimension and additional inertia weight factor of Bat Algorithm proves to be more effective than the basic Bat Algorithm in terms of searching for a solution while improving quality of results in all cases or significantly improving convergence speed.

Item Type:	Article
Uncontrolled Keywords:	Bat Algorithm, Exploration and exploitation, Iteration, Metaheuristic
Divisions:	Faculty of Electronics and Computer Engineering
Depositing User:	Sabariah Ismail
Date Deposited:	22 Dec 2020 11:23
Last Modified:	12 Jun 2023 11:38
URI:	http://eprints.utem.edu.my/id/eprint/24451
Statistic Details:	View Download Statistic

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