A Novel Hybrid Binary Bat Algorithm for Global Optimization

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Author(s): Huijun Liang (Hubei Minzu University, China), Aokang Pang (Hubei Minzu University, China), Chenhao Lin (Hubei Minzu University, China)and Jianwei Zhong (Hubei Minzu University, China)
Copyright: 2024
Volume: 15
Issue: 1
Pages: 29
Source title: International Journal of Swarm Intelligence Research (IJSIR)
Editor(s)-in-Chief: Yuhui Shi (Southern University of Science and Technology (SUSTech), China)
DOI: 10.4018/IJSIR.342098

Keywords: Artificial Intelligence / Collective Intelligence / Computer Science & IT / Information Science Reference

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Abstract

In this article, a novel hybrid binary bat algorithm named HBBA is proposed for global optimization problems. First, to avoid simultaneous updating of bat velocity's dimensional components, i.e., elements of velocity vector, a random black hole model is modified to adapt to binary algorithm for updating in unknown spaces for each dimensional component individually. Through this way, the search ability of bats around the current group best is increased greatly. Second, a time-varying v-shaped transfer function, rather than a time-invariant one as in closely related works, is proposed to map velocity in continuous search space to a binary one. This accelerates the speed to switch individuals' positions, i.e., solutions in binary space. Third, a chaotic map is utilized to replace monotonous parameters in original binary bat algorithm, which is beneficial for avoiding premature convergence. Simulation results demonstrate the effectiveness of the proposed algorithm by three types of benchmark functions and unit commitment problem.

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A Novel Hybrid Binary Bat Algorithm for Global Optimization

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