Parameter tuning of the HCSCROCFO-3Opt algorithm for solving the capacitated vehicle routing problem

Abstract: This paper proposes the cuckoo search (CS), central force optimization (CFO), chemical reaction optimization (CRO) and 3-Opt for solving the capacitated vehicle routing problem (CVRP). HCSCROCFO-3Opt, which is the parallel hybrid algorithm that is proposed, is a form of augmented HCSCROCFO with a local search process founded on CS that utilizes positive aspects of the other optimization approaches including CRO and CFO in order to enhance quality of initial population and improve local search, correspondingly. The work is motivated by the need to enhance the computational effectiveness through attainment of improved outcomes compared to previous popular solutions, to explore the features of different parameters of to seek some ideal solutions. The first stage entails solving of CVRP through setting a variety of values to tune parameters for the HCSCROCFO-3Opt proposed. Then initialization of algorithm CS, CRO, CFO parameters are accomplished through tuning parameters within a tuning cycle. Subsequently, a novel solution is swapped in a random manner through a levy flight within the central loop, followed by execution of the hybrid solution as well as new CRO, CFO and CS algorithm solutions, whose implementation is supposed to enhance results for the local 3-Opt. Ultimately, the most ideal solution for general hybrid model’s solution space is identified, after which the solution that is best-suited for the CVRP purposes is presented. Within the standard CVRP cases, reported computational tests in large scale in the literature demonstrate the efficiency of presented approach.

How to cite this paper

 Saeheaw, T. (2020). Parameter tuning of the HCSCROCFO-3Opt algorithm for solving the capacitated vehicle routing problem.International Journal of Industrial Engineering Computations , 11(4), 481-490.

Refrences

Belfiore, P., Tsugunobu, H., & Yoshizaki, Y., (2008). Chapter 1 Scatter search for vehicle routing problem with time windows and split deliveries, in: Caric, T., Gold, H. (Eds.), Vehicle routing problem.). I-Tech Education and Publishing KG, Vienna, pp. 1–14.
Blazinskas, A., & Misevicius, A. (2011). combining 2-opt, 3-opt and 4-opt with k-swap-kick perturbations for the traveling salesman problem. Kaunas University of Technology, Department of Multimedia Engineering, Studentu St, 50-401.
Blum, C., Puchinger, J., Raidl, G. R., & Roli, A. (2010). A brief survey on hybrid metaheuristics. Proceedings of BIOMA, 3-18.
Blum, C., Puchinger, J., Raidl, G.R.,& Roli, A. (2011). Hybrid metaheuristics in combinatorial optimization: A survey. Applied Soft Computing, 11, 4135–4151.
Blum, C.,& Roli, A. (2003). Metaheuristics in combinatorial optimization. ACM Computing Surveys, 35, 268–308.
Dantzig, G.B.,& Ramser, J.H. (1959). The truck dispatching problem. Management Science, 6, 80–91.
Formato, R.A. (2007). Central Force Optimization: A new metaheuristic with applications in applied electromagnetics. Progress In Electromagnetics Research, 77, 425–491.
Jin, J.G., Crainic, T.G.,& Løkketangen, A.G. (2014). A cooperative parallel metaheuristic for the capacitated vehicle routing problem. Computers & Operations Research, 44, 33–41.
Juan, A.A., Faulin, J., Ruiz, R., Barrios, B.,& Caballé, S. (2010). The SR-GCWS hybrid algorithm for solving the capacitated vehicle routing problem. Applied Soft Computing, 10, 215–224.
Lam, A.Y.S.,& Li, V.O.K. (2010). Chemical-reaction-inspired metaheuristic for optimization. IEEE Transactions on Evolutionary Computation, 14, 381–399.
Lam, A. Y., Xu, J., & Li, V. O. (2010, July). Chemical reaction optimization for population transition in peer-to-peer live streaming. In IEEE Congress on Evolutionary Computation (pp. 1-8). IEEE.
Marlow, D. O., Kilby, P., & Mercer, G. N. (2007, December). The travelling salesman problem in maritime surveillance–techniques, algorithms and analysis. In Proceedings of the international congress on modelling and simulation (pp. 684-690).
Ólafsson, S., (2006). Chapter 21 metaheuristics, in simulation. in: Henderson, S.G., Nelson, B.L. (Eds.), Handbooks in operations research and management science. Elsevier, Amsterdam, pp. 633–654.
Saeheaw, T.,& Charoenchai, N. (2018). A comparative study among different parallel hybrid artificial intelligent approaches to solve the capacitated vehicle routing problem. International Journal of Bio-Inspired Computation, 11, 171–191.
Urli, T. (2015). Hybrid meta-heuristics for combinatorial optimization [dissertation]. Udine (Italy): Università degli Studi di Udine.
Vidal, M.A., Moreno, P.I., & Poulin, E. (2012). Genetic diversity and insular colonization of Liolaemus Picts’ (Squamata, Liolaeminae) in northwestern Patagonia. Austral Ecology, 37, 67–77.
Yang, X. S. (2010). Nature-inspired metaheuristic algorithms. Luniver press.
Yang, X. S., & Deb, S. (2009, December). Cuckoo search via Lévy flights. In 2009 World congress on nature & biologically inspired computing (NaBIC) (pp. 210-214). IEEE.
Zhou, Y., & Xie, J., Zheng, H. (2013). A hybrid bat algorithm with path relinking for capacitated vehicle routing problem. Mathematical Problems in Engineering, 2013, 1–10.