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An Optimized Discrete Dragonfly Algorithm Tackling the Low Exploitation Problem for Solving TSP

Author

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  • Bibi Aamirah Shafaa Emambocus

    (Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, Petaling Jaya 47500, Selangor, Malaysia)

  • Muhammed Basheer Jasser

    (Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, Petaling Jaya 47500, Selangor, Malaysia)

  • Angela Amphawan

    (Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, Petaling Jaya 47500, Selangor, Malaysia)

  • Ali Wagdy Mohamed

    (Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt)

Abstract

Optimization problems are prevalent in almost all areas and hence optimization algorithms are crucial for a myriad of real-world applications. Deterministic optimization algorithms tend to be computationally costly and time-consuming. Hence, heuristic and metaheuristic algorithms are more favoured as they provide near-optimal solutions in an acceptable amount of time. Swarm intelligence algorithms are being increasingly used for optimization problems owing to their simplicity and good performance. The Dragonfly Algorithm (DA) is one which is inspired by the swarming behaviours of dragonflies, and it has been proven to have a superior performance than other algorithms in multiple applications. Hence, it is worth considering its application to the traveling salesman problem which is a predominant discrete optimization problem. The original DA is only suitable for solving continuous optimization problems and, although there is a binary version of the algorithm, it is not easily adapted for solving discrete optimization problems like TSP. We have previously proposed a discrete adapted DA algorithm suitable for TSP. However, it has low effectiveness, and it has not been used for large TSP problems. In this paper, we propose an optimized discrete adapted DA by using the steepest ascent hill climbing algorithm as a local search. The algorithm is applied to a TSP problem modelling a package delivery system in the Kuala Lumpur area and to benchmark TSP problems, and it is found to have a higher effectiveness than the discrete adapted DA and some other swarm intelligence algorithms. It also has a higher efficiency than the discrete adapted DA.

Suggested Citation

  • Bibi Aamirah Shafaa Emambocus & Muhammed Basheer Jasser & Angela Amphawan & Ali Wagdy Mohamed, 2022. "An Optimized Discrete Dragonfly Algorithm Tackling the Low Exploitation Problem for Solving TSP," Mathematics, MDPI, vol. 10(19), pages 1-24, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3647-:d:934174
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    References listed on IDEAS

    as
    1. Muren, & Wu, Jianjun & Zhou, Li & Du, Zhiping & Lv, Ying, 2019. "Mixed steepest descent algorithm for the traveling salesman problem and application in air logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 126(C), pages 87-102.
    2. Mehdi Foumani & Asghar Moeini & Michael Haythorpe & Kate Smith-Miles, 2018. "A cross-entropy method for optimising robotic automated storage and retrieval systems," International Journal of Production Research, Taylor & Francis Journals, vol. 56(19), pages 6450-6472, October.
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