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A hybrid cuckoo search-genetic algorithm for hole-making sequence optimization

Author

Listed:
  • W. C. E. Lim

    (Monash University Malaysia, Jalan Lagoon Selatan)

  • G. Kanagaraj

    (Thiagarajar College of Engineering)

  • S. G. Ponnambalam

    (Monash University Malaysia, Jalan Lagoon Selatan)

Abstract

Biologically-inspired algorithms are stochastic search methods that emulate the behavior of natural biological evolution to produce better solutions and have been widely used to solve engineering optimization problems. In this paper, a new hybrid algorithm is proposed based on the breeding behavior of cuckoos and evolutionary strategies of genetic algorithm by combining the advantages of genetic algorithm into the cuckoo search algorithm. The proposed hybrid cuckoo search-genetic algorithm (CSGA) is used for the optimization of hole-making operations in which a hole may require various tools to machine its final size. The main objective considered here is to minimize the total non-cutting time of the machining process, including the tool positioning time and the tool switching time. The performance of CSGA is verified through solving a set of benchmark problems taken from the literature. The amount of improvement obtained for different problem sizes are reported and compared with those by ant colony optimization, particle swarm optimization, immune based algorithm and cuckoo search algorithm. The results of the tests show that CSGA is superior to the compared algorithms.

Suggested Citation

  • W. C. E. Lim & G. Kanagaraj & S. G. Ponnambalam, 2016. "A hybrid cuckoo search-genetic algorithm for hole-making sequence optimization," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 417-429, April.
    • Handle: RePEc:spr:joinma:v:27:y:2016:i:2:d:10.1007_s10845-014-0873-z
    DOI: 10.1007/s10845-014-0873-z
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    References listed on IDEAS

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    1. G. A. Croes, 1958. "A Method for Solving Traveling-Salesman Problems," Operations Research, INFORMS, vol. 6(6), pages 791-812, December.
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    Cited by:

    1. 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.
    2. Raghav Prasad Parouha & Pooja Verma, 2022. "An innovative hybrid algorithm for bound-unconstrained optimization problems and applications," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1273-1336, June.
    3. Vijay Rathod, 2023. "Multi-drill path sequencing models: A comparative study," OPSEARCH, Springer;Operational Research Society of India, vol. 60(1), pages 554-570, March.
    4. Sehyun Tak & Jeongyun Kim & Donghoun Lee, 2022. "Study on the Extraction Method of Sub-Network for Optimal Operation of Connected and Automated Vehicle-Based Mobility Service and Its Implication," Sustainability, MDPI, vol. 14(6), pages 1-28, March.
    5. Sunny Diyaley & Abhiraj Aditya & Shankar Chakraborty, 2020. "Optimization of the multi-hole drilling path sequence for concentric circular patterns," OPSEARCH, Springer;Operational Research Society of India, vol. 57(3), pages 746-764, September.

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