Title: Bat algorithm with Weibull walk for solving global optimisation and classification problems

Authors: Hafiz Tayyab Rauf; Muhammad Hadi; Abdur Rehman

Addresses: Department of Computer Science, University of Gujrat, Gujrat, Pakistan ' Department of Computer Science, University of Gujrat, Gujrat, Pakistan ' Department of Computer Science, University of Gujrat, Gujrat, Pakistan

Abstract: Bat algorithm (BA) becomes the most widely employed meta-heuristic algorithm to interpret the diverse kind of optimisation and real-world classification problems. BA suffers from one of the influential challenges called local minima. In this study, we carry out two modifications in the original BA and proposed a modified variant of BA called bat algorithm with Weibull walk (WW-BA) to solve the premature convergence issue. The first modification involves the introduction of Weibull descending inertia weight for updating the velocity of bats. The second modification approach updates the local search strategy of BA by replacing the Random walk with the Weibull Walk. The simulation performed on 19 standard benchmark functions represent the competence and effectiveness of WW-BA compared to the state of the art techniques. The proposed WWBA is also examined for classification problem. The empirical results reveal that the proposed technique outperformed the classical techniques.

Keywords: bat algorithm; premature convergence; exploration; exploitation; Weibull walk; inertia weight.

DOI: 10.1504/IJBIC.2020.107470

International Journal of Bio-Inspired Computation, 2020 Vol.15 No.3, pp.159 - 170

Received: 08 Nov 2018
Accepted: 24 Jul 2019
Published online: 29 May 2020 *

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