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Enhanced intelligent water drops algorithm for multi-depot vehicle routing problem

Author

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  • Absalom E Ezugwu
  • Francis Akutsah
  • Micheal O Olusanya
  • Aderemi O Adewumi

Abstract

The intelligent water drop algorithm is a swarm-based metaheuristic algorithm, inspired by the characteristics of water drops in the river and the environmental changes resulting from the action of the flowing river. Since its appearance as an alternative stochastic optimization method, the algorithm has found applications in solving a wide range of combinatorial and functional optimization problems. This paper presents an improved intelligent water drop algorithm for solving multi-depot vehicle routing problems. A simulated annealing algorithm was introduced into the proposed algorithm as a local search metaheuristic to prevent the intelligent water drop algorithm from getting trapped into local minima and also improve its solution quality. In addition, some of the potential problematic issues associated with using simulated annealing that include high computational runtime and exponential calculation of the probability of acceptance criteria, are investigated. The exponential calculation of the probability of acceptance criteria for the simulated annealing based techniques is computationally expensive. Therefore, in order to maximize the performance of the intelligent water drop algorithm using simulated annealing, a better way of calculating the probability of acceptance criteria is considered. The performance of the proposed hybrid algorithm is evaluated by using 33 standard test problems, with the results obtained compared with the solutions offered by four well-known techniques from the subject literature. Experimental results and statistical tests show that the new method possesses outstanding performance in terms of solution quality and runtime consumed. In addition, the proposed algorithm is suitable for solving large-scale problems.

Suggested Citation

  • Absalom E Ezugwu & Francis Akutsah & Micheal O Olusanya & Aderemi O Adewumi, 2018. "Enhanced intelligent water drops algorithm for multi-depot vehicle routing problem," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-32, March.
  • Handle: RePEc:plo:pone00:0193751
    DOI: 10.1371/journal.pone.0193751
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    Cited by:

    1. Ranka Gojković & Goran Đurić & Danijela Tadić & Snežana Nestić & Aleksandar Aleksić, 2021. "Evaluation and Selection of the Quality Methods for Manufacturing Process Reliability Improvement—Intuitionistic Fuzzy Sets and Genetic Algorithm Approach," Mathematics, MDPI, vol. 9(13), pages 1-17, June.
    2. Shejun Deng & Yingying Yuan & Yong Wang & Haizhong Wang & Charles Koll, 2020. "Collaborative multicenter logistics delivery network optimization with resource sharing," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-31, November.
    3. Yanjun Shi & Na Lin & Qiaomei Han & Tongliang Zhang & Weiming Shen, 2020. "A Method for Transportation Planning and Profit Sharing in Collaborative Multi-Carrier Vehicle Routing," Mathematics, MDPI, vol. 8(10), pages 1-23, October.
    4. Wang, Yuan & Lei, Linfei & Zhang, Dongxiang & Lee, Loo Hay, 2020. "Towards delivery-as-a-service: Effective neighborhood search strategies for integrated delivery optimization of E-commerce and static O2O parcels," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 38-63.

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