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An upgraded firefly algorithm with feasibility-based rules for constrained engineering optimization problems

Author

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  • Ivona Brajević

    (University of Niš)

  • Jelena Ignjatović

    (University of Niš)

Abstract

The firefly algorithm (FA) has become one of the most prominent swarm intelligence methods due to its efficiency in solving a wide range of various real-world problems. In this paper, an upgraded firefly algorithm (UFA) is proposed to further improve its performance in solving constrained engineering optimization problems. The main modifications of the basic algorithm are the incorporation of the logistic map and reduction scheme mechanism in order to perform fine adjustments of its control parameters, and employing a mutation operator in order to provide useful diversity in the population. Also, the proposed approach uses certain feasibility-based rules in order to guide the search to the feasible region of the search space, the improved scheme to handle the boundary constraints and the method for handling equality constraints. The UFA is tested on a set of 24 benchmark functions presented in CEC’2006 and nine widely used constrained engineering optimization problems. Comprehensive experimental results show that the overall performance of the UFA is superior to the FA and its recently proposed variants. Moreover, it achieves highly competitive results compared with other state-of-the-art metaheuristic techniques.

Suggested Citation

  • Ivona Brajević & Jelena Ignjatović, 2019. "An upgraded firefly algorithm with feasibility-based rules for constrained engineering optimization problems," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2545-2574, August.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:6:d:10.1007_s10845-018-1419-6
    DOI: 10.1007/s10845-018-1419-6
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    References listed on IDEAS

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    1. Fister, Iztok & Perc, Matjaž & Kamal, Salahuddin M. & Fister, Iztok, 2015. "A review of chaos-based firefly algorithms: Perspectives and research challenges," Applied Mathematics and Computation, Elsevier, vol. 252(C), pages 155-165.
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    5. Alejandro Alvarado-Iniesta & Jorge L. García-Alcaraz & Manuel Piña-Monarrez & Luis Pérez-Domínguez, 2016. "Multiobjective optimization of torch brazing process by a hybrid of fuzzy logic and multiobjective artificial bee colony algorithm," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 631-638, June.
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    Cited by:

    1. Lenin Nagarajan & Siva Kumar Mahalingam & Jayakrishna Kandasamy & Selvakumar Gurusamy, 2022. "A novel approach in selective assembly with an arbitrary distribution to minimize clearance variation using evolutionary algorithms: a comparative study," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1337-1354, June.
    2. Yiying Zhang & Aining Chi, 2023. "Group teaching optimization algorithm with information sharing for numerical optimization and engineering optimization," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1547-1571, April.
    3. Anshuman Kumar Sahu & Siba Sankar Mahapatra, 2021. "Prediction and optimization of performance measures in electrical discharge machining using rapid prototyping tool electrodes," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2125-2145, December.
    4. Sujata Dash & Ajith Abraham & Ashish Kr Luhach & Jolanta Mizera-Pietraszko & Joel JPC Rodrigues, 2020. "Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis," International Journal of Distributed Sensor Networks, , vol. 16(1), pages 15501477198, January.
    5. Yiying Zhang & Zhigang Jin, 2022. "Comprehensive learning Jaya algorithm for engineering design optimization problems," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1229-1253, June.
    6. Wenchao Yi & Liang Gao & Zhi Pei & Jiansha Lu & Yong Chen, 2021. "ε Constrained differential evolution using halfspace partition for optimization problems," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 157-178, January.

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