A Modified Bat Algorithm Based on Gaussian Distribution for Solving Optimization Problem
An improved Bat algorithm with Gaussian distribution random walk (BAGD) is introduced in this paper. The original Bat algorithm has a problem of random large step length that leads to suboptimal solutions in the search space and it cannot solve higher dimensional problems. To solve
higher dimensional problems and to decrease the step length size, this research focuses on using a Gaussian distribution in Bat algorithm which provide shorter step lengths during the search. The proposed BAGD was compared with six popular metaheuristic algorithms on ten benchmark functions.
Comparative results indicated that the proposed BAGD perform better than the state-of-the-art algorithms in most cases. The proposed BAGD solution used small step lengths in the search space and it was able to solve high dimensional problems.
Keywords: Bat Algorithm; Benchmark Functions; Gaussian Distribution; Numerical Optimization
Document Type: Research Article
Affiliations: 1: Software and Multimedia Centre, Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia 2: Faculty of Computer Science and Information Technology, University of Malaya, 50603 Lembah Pantai, Kuala Lumpur, Malaysia
Publication date: 01 January 2016
- Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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