Abstract
In this paper, a modification in randomness factor is proposed for enhancing the exploitation capability of firefly algorithm. The proposed approach is applied on Vehicle Routing Problem with Time Windows (VRPTW). There is no single distance measure that fits for all type of VRPTW. An attempt has been made to evaluate on three different distance measures on the proposed approach. The performance of proposed approach on different distance measures has been evaluated on two well-known instances of Solomon’s benchmark test. The experimental results show that the performance of Brute–Curtis distance measure outperforms the other measures.
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References
Golden BL, Assad A (1988) Vehicle routing: methods and studies. Elsevier Science Publishers, UK
Hassanzadeh T, Faez K, Seyfi G (2012) A speech recognition system based on structure equivalent fuzzy neural network trained by firefly algorithm. In: Proceedings of IEEE international conference on biomedical engineering, pp 63–67
Glover F (1989) Tabu search. Part 1, ORSA. J Comput 1(3):190–206
Pirlot M (1996) General local search methods. Eur J Oper 92:493–511
X.S. Nature-inspired metaheuristic algorithms. Luniver Press, Bristol, 2008
Kumar V, Kumar D (2014) Performance evaluation of distance metrics in the clustering algorithms. INFOCOMP J Comput Sci 13(1):38–52
Sayadi M, Ramezanian R, Ghaffari-Nasab N (2010) A discrete firefly meta-heuristic with local search for makespan minimization in permutation flow shop scheduling problems. Int J Ind Eng Comput 1(1):1–10
Pullen H, Webb M (1967) A computer application to a transport scheduling problem. Comput J 10:10–13
Madsen OBG (1976) Optimal scheduling of trucks—a routing problem with tight due times for delivery. In: Strobel H, Genser R, Etschmaier M (eds) Optimization applied to transportation systems. IIASA, International Institute for Applied System Analysis, Laxenburgh, pp 126–136
Knight K, Hofer J (1968) Vehicle scheduling with timed and connected calls: a case study. Oper Res Q 19:299–310
Golden BL, Assad AA (1986) Perspectives on vehicle routing: exciting new developments. Oper Res 34:803–809
Desrochers M, Lenstra JK, Savelsbergh MWP, Soumis F (1988) Vehicle routing with time windows: optimization and approximation. In: Golden B, Assad A (eds) Vehicle routing: methods and studies. Elsevier Science Publishers, UK, pp 65–84
Chiang W, Russell R (1996) Simulated annealing metaheuristics for the vehicle routing problem with time windows. Ann Oper Res 63:3–27
Osman I (1993) Metastrategy simulated annealing and tabu search heuristic algorithms for the vehicle routing problem. Ann Oper Res 41:421–434
Cordeau J-F, Desaulniers G, Desrosiers J, Solomon MM, Soumis F (2000) The VRP with Time Windows. Les Cahiers du GERAD
Yang XS (2011) Metaheuristic optimization: algorithm analysis and open problems. Experimental algorithms. Springer, Berlin, pp 21–32
Yang XS (2012) Efficiency analysis of swarm intelligence and randomization techniques. J Comput Theoret Nanosci 9(2):189–198
Das S, Maity S, Qu BY, Suganthan PN (2011) Real-parameter evolutionary multimodal optimization survey of the state-of-the-art. Swarm Evol Comput 1(2):71–88
Yang XS (2009) Firefly algorithms for multimodal optimization. Stochastic algorithms: foundations and applications. Springer, UK, pp 169–178
Abedinia O, Amjady N, Naderi MS (2012) Multi-objective environmental/economic dispatch using firefly technique. In: Proceedings of IEEE international conference on environment and electrical engineering, pp 461–466
Durkota K (2011) Implementation of a discrete firefly algorithm for the qap problem within the sage framework. BSc thesis, Czech Technical University
Marichelvam MK, Prabaharan T, Yang XS (2014) A discrete firefly algorithm for the multiobjective hybrid flowshop scheduling problems. EEE Trans Evol Comput 18(2):301–305
Osaba E, Carballedo R, Yang XS, Diaz F (2016) An evolutionary discrete firefly algorithm with novel operators for solving the vehicle routing problem with time windows. In: Nature-inspired computation in engineering, pp 21–41
Tilahun SL, Ong HC (2012) Modified firefly algorithm. J Appl Math. https://doi.org/10.1155/2012/467631
Gandomi A, Yang XS, Talatahari S, Alavi A (2013) Firefly algorithm with chaos. Commun Nonlinear Sci Numer Simul 18(1):89–98
Coelho LDS, de Andrade Bernert DL, Mariani VC (2011) A chaotic firefly algorithm applied to reliability-redundancy optimization. In: Proceedings of IEEE congress on evolutionary computation, pp 517–521
Ma L, Cao P (2016) Comparative study of several improved firefly algorithms. In: IEEE International Conference on Information and Automation (ICIA), IEEE, Ningbo
Goel R, Maini R (2018) A hybrid of ant colony and firefly algorithms (HAFA) for solving vehicle routing problems. J Comput Sci 25:28–37
Yelghi A, Kose C (2018) A modified firefly algorithm for global minimum optimization. Appl Soft Comput 62:29–44
Tighzert L, Fonlupt C, Mendil B (2018) A set of new compact firefly algorithms. Swarm Evol Comput 40:92–115
Yang XS (2010) Firefly algorithm, stochastic test functions and design optimisation. Bio-Inspired Comput 2(2):78–84
Yang XS, Deb S (2010) Eagle strategy using lévy walk and firefly algorithms for stochastic optimization. Stud Comput Intell 284:101–111
Solomon MM (1987) Algorithms for the vehicle routing and scheduling problems with time window constraints. Oper Res 35:254–265
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Aggarwal, D., Kumar, V. Performance evaluation of distance metrics on Firefly Algorithm for VRP with time windows. Int. j. inf. tecnol. 13, 2355–2362 (2021). https://doi.org/10.1007/s41870-019-00387-7
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DOI: https://doi.org/10.1007/s41870-019-00387-7