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A hybrid bat algorithm with natural-inspired algorithms for continuous optimization problem

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Abstract

This paper proposes a hybrid bat algorithm with natural-inspired algorithms for continuous optimization problem. In this study, the proposed algorithm combines the reproduction step from weed algorithm and genetic algorithm. The reproduction step is applied to clone each bat population by fitness values and the genetic algorithm is applied in order to expand the population. The algorithm is evaluated on eighteen benchmark problems. The computational results of the proposed algorithm are compared with the methods in the literature which are self-adaptive differential evolution (DE), traditional DE algorithm, intersection mutation differential evolution (IMDE) algorithm, and the JDE self-adaptive algorithm. Findings show that the algorithm produces several solutions obtained by the previously published methods especially for the continuous unimodal function, the quartic function, the multimodal function and the discontinuous step function. In addition, the finding shows that the proposed algorithm can produce optimal solutions efficiently on benchmark instances within short computational time.

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Acknowledgments

The authors would like to acknowledge School of Information and Communication Technology, University of Phayao for all resources and highly grateful to Associate Professor Dr. Arit Thammano and Dr. Ravee Phoewhawm for the technical assistance.

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Authors and Affiliations

  1. School of Information and Communication Technology, University of Phayao, Phayao, 56000, Thailand

    Sakkayaphop Pravesjit

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  1. Sakkayaphop Pravesjit
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Correspondence to Sakkayaphop Pravesjit.

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Pravesjit, S. A hybrid bat algorithm with natural-inspired algorithms for continuous optimization problem. Artif Life Robotics 21, 112–119 (2016). https://doi.org/10.1007/s10015-015-0248-3

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  • DOI: https://doi.org/10.1007/s10015-015-0248-3

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