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Meliorated Crab Mating Optimization Algorithms for Capacitated Vehicle Routing Problem

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Abstract

This study proposes a new metaheuristic optimization algorithm, inspired by crabs mating in nature, with five versions. For these crab versions, first, the code of the crab mating optimization algorithm was written, inspired by Chifu’s crab mating optimization paper. It has been observed that the original crab mating algorithm gives successful results; however, works very slowly and there are some parameters that are not used in the algorithm, and the mating probability of crabs converges to either 100% or 0%. Considering that the crab mating algorithm gives good results, new crab versions have been developed from this algorithm. The improved crab algorithms are compared with 4 popular metaheuristic algorithms for 20 different benchmark functions on metrics, such as mean, standard deviation, optimality, accuracy, run time, and the number of function evaluations (NFE). According to the results obtained, the proposed crab versions give as good results as the popular algorithms. In the last part of the study, the proposed algorithms were adapted for capacitated Vehicle Routing Problem (VRP) which is one of the real-world optimization problems, and their performances on this problem were compared among themselves. As a result of this comparison made on the VRP, the Meliorated Adaptive Crab mating optimization algorithm (MAC) algorithm gave more successful results in terms of speed and performance than the other proposed crab versions. Due to the performance of the proposed algorithms, we expect these algorithms to be applied to different optimization problems.

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The datasets used during the current study are available from the corresponding author upon reasonable request.

References

  1. Beheshti Z, Shamsuddin SMH. A review of population-based meta-heuristic algorithms. Int J Adv Soft Comput Appl. 2013;5(1):1–35.

    Google Scholar 

  2. Buscher U, Mayer B, Ehrig T. A genetic algorithm for the unequal area facility layout problem. In: Operations Research Proceedings 2012: Selected Papers of the International Annual Conference of the German Operations Research Society (GOR), Leibniz University of Hannover, Germany; 2014. p. 109–114.

  3. Razali NM, Geraghty J. Genetic algorithm performance with different selection strategies in solving tsp. In: Proceedings of The World Congress on Engineering; 2011. p. 1–6.

  4. Kirkpatrick S, Gelatt CD Jr, Vecchi MP. Optimization by simulated annealing. Science. 1983;220(4598):671–80.

    Article  MathSciNet  MATH  Google Scholar 

  5. Kirkpatrick S. Optimization by simulated annealing: quantitative studies. J Statist Phys. 1984;34:975–86.

    Article  MathSciNet  Google Scholar 

  6. Karaboga DEA. An idea based on honey bee swarm for numerical optimization. Technical report, Technical report-tr06, Erciyes University; 2005.

  7. Karaboga D, Basturk B. On the performance of artificial bee colony (abc) algorithm. Appl Soft Comput. 2008;8(1):687–97.

    Article  Google Scholar 

  8. Karaboga D, Gorkemli B, Ozturk C, Karaboga N. A comprehensive survey: artificial bee colony (abc) algorithm and applications. Artific Intellig Rev. 2014;42:21–57.

    Article  Google Scholar 

  9. Caniyilmaz E, Benli B, Ilkay MS. An artificial bee colony algorithm approach for unrelated parallel machine scheduling with processing set restrictions, job sequence-dependent setup times, and due date. Int J Adv Manuf Technol. 2015;77:2105–15.

    Article  Google Scholar 

  10. Kennedy J, Eberhart R. Particle swarm optimization; 1995. p. 1942–1948.

  11. Moslehi G, Mahnam M. A pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search. Int J Prod Econ. 2011;129(1):14–22.

    Article  Google Scholar 

  12. Storn R, Price K. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optimiz. 1997;11(4):341.

    Article  MathSciNet  MATH  Google Scholar 

  13. Price K, Storn RM, Lampinen JA. Differential Evolution: a Practical Approach to Global Optimization. Berlin: Springer; 2006.

    MATH  Google Scholar 

  14. Yüzgeç U. Performance comparison of differential evolution techniques on optimization of feeding profile for an industrial scale baker’s yeast fermentation process. ISA Transact. 2010;49(1):167–76.

    Article  Google Scholar 

  15. Dorigo M, Maniezzo V, Colorni A. Ant system: optimization by a colony of cooperating agents. IEEE Transact Syst Man Cybernet Part B (Cybernetics). 1996;26(1):29–41.

    Article  Google Scholar 

  16. Dorigo M, Birattari M, Stutzle T. Ant colony optimization. IEEE Comput Intellig Magaz. 2006;1(4):28–39.

    Article  Google Scholar 

  17. Chifu VR, et al. Crab mating optimization algorithm. In: 2014 18th International Conference on System Theory, Control and Computing (ICSTCC); 2014. p. 353–358.

  18. Çubukçu B, Yüzgeç U. Monogamous crab mating optimization algorithm. In: 2017 International Conference on Computer Science and Engineering (UBMK); 2017. p. 60–65.

  19. Çubukçu B, Yüzgeç U. Monogamous crab mating optimization algorithm for solving vehicle routing problem. In: International Conference on Advanced Technologies, Computer Engineering and Science (ICATCES’18); 2018. p. 49–52.

  20. Yesilyurt IN. A Study Is on the Settlement of Blue Crab (callinectes Sapidus) Postlarvae (megalopae) on the North Coast of Yumurtalik Cove.

  21. The University of Waikato. Crab life cycle. Available at http://www.sciencelearn.org.nz/images/601-crab-life-cycle; 2023.

  22. Caric T, Gold H. Vehicle Routing Problem. London: IntechOpen; 2008.

    Book  Google Scholar 

  23. Goel A, Gruhn V. A general vehicle routing problem. Eur J Operat Res. 2008;191(3):650–60.

    Article  MathSciNet  MATH  Google Scholar 

  24. Toth P, Vigo D. The Vehicle Routing Problem. Society for Industrial and Applied Mathematics; 2002.

  25. Luo J, Li X, Chen MR, Liu H. A novel hybrid shuffled frog leaping algorithm for vehicle routing problem with time windows. Inform Sci. 2015;316:266–92.

    Article  Google Scholar 

  26. Luo W, Li Y. Benchmarking heuristic search and optimisation algorithms in matlab. In: 22nd International Conference on Automation and Computing (ICAC); 2016. p. 250–255.

  27. Heris MK. Capacitated Vehicle Routing Problem (VRP) using SA. https://yarpiz.com/372/ypap108-vehicle-routing-problem. Yarpiz; 2015.

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Correspondence to Ugur Yuzgec.

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Cubukcu, B., Yuzgec, U. Meliorated Crab Mating Optimization Algorithms for Capacitated Vehicle Routing Problem. SN COMPUT. SCI. 5, 79 (2024). https://doi.org/10.1007/s42979-023-02385-w

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