Abstract
The bat algorithm (BA) is a recent swarm intelligence algorithm which can be a powerful tool for numerical optimization. However, the BA and most of its variants rely on a quite elitist strategy where the bat with the current global best fitness guides the population’s search direction. This may result in a slow convergence rate and low accuracy when searching in complex problem spaces. In this chapter, a dimension-based best bat for BA is formed by integrating both the current global best bat and favorable search information of the other bats in different dimensions separately. This strategy is embedded in the BA to guide the population to fly toward better directions. The proposed algorithm is tested on the IEEE CEC 2017 benchmark suite and is compared with some related algorithms. The experimental results demonstrate the effectiveness and robustness of the proposed BA enhancement.
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Acknowledgements
The authors thank the Chinese National Natural Science Foundation (No. 61972424), the fund of the China Scholarship Council in 2019, and the Fundamental Research Funds for the Central Universities, South-Central University for Nationalities (No. CZY18012), for partial financial support for this work.
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Zhou, L., Smith, A.E. (2022). An Improved Bat Algorithm with a Dimension-Based Best Bat for Numerical Optimization. In: Smith, A.E. (eds) Women in Computational Intelligence. Women in Engineering and Science. Springer, Cham. https://doi.org/10.1007/978-3-030-79092-9_19
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DOI: https://doi.org/10.1007/978-3-030-79092-9_19
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