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Using chaos enhanced hybrid firefly particle swarm optimization algorithm for solving continuous optimization problems

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Abstract

Optimization becomes more important and the use of optimization methods is becoming widespread with the developments in computer sciences. Researchers from different scientific fields are looking for better solutions to solve complex problems with optimization methods. In some complex problems, optimal results can be obtained utilizing metaheuristic algorithms. Researchers carry out different studies to improve the performance of present metaheuristic algorithms. Although the success of metaheuristic algorithms has been seen in previous studies, there are some weaknesses in these algorithms. Therefore, successful results cannot be obtained for each problem sometimes. In order to overcome this problem, more successful algorithms can be obtained by hybridizing the strong points of the different methods together. In addition, one of the important factors affecting the success of optimization algorithms is scanning ability of the solution space in order to find the optima. Exploring search space is carried out using random variables by some metaheuristic algorithms. The chaotic values that are generated by chaotic maps can be used instead of random values. Thus, search ability of algorithms performs more dynamically. In this study, hybrid firefly and particle swarm optimization algorithms are transformed to a chaotic-based algorithm by use of 10 different chaotic maps. Random valued parameters are generated by chaotic maps. In order to indicate the performances between different dimensions, CEC 2015 benchmark and constraint problems are used in experimental studies. Chaos enhanced methods are compared against canonical and hybrid optimization algorithms. It has been seen that obtained results of the proposed method were sufficiently successful and reliable.

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Acknowledgements

This study was supported by The Scientific and Technological Research Council of Turkey (TUBITAK), the project number 118E355. The numerical calculations reported in this paper were partially performed at Harran University High Performance Computing (Harran HPC) laboratory. The authors thank the TUBITAK and Harran HPC for all the support.

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Authors and Affiliations

  1. Computer Engineering Department, Engineering Faculty, Harran University, Şanlıurfa, Turkey

    İbrahim Berkan Aydilek & Mehmet Emin Tenekeci

  2. Industrial Engineering Department, Engineering Faculty, Harran University, Şanlıurfa, Turkey

    İzzettin Hakan Karaçizmeli & Serkan Kaya

  3. Electrical-Electronics Engineering Department, Engineering Faculty, Harran University, Şanlıurfa, Turkey

    Abdülkadir Gümüşçü

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  1. İbrahim Berkan Aydilek
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  2. İzzettin Hakan Karaçizmeli
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  5. Abdülkadir Gümüşçü
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Correspondence to Mehmet Emin Tenekeci.

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Aydilek, İ.B., Karaçizmeli, İ.H., Tenekeci, M.E. et al. Using chaos enhanced hybrid firefly particle swarm optimization algorithm for solving continuous optimization problems. Sādhanā 46, 65 (2021). https://doi.org/10.1007/s12046-021-01572-w

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