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Heuristic Search pp 157–188Cite as

Palgrave Macmillan

Implementation Issues

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Abstract

In this chapter, I first provide some implementation issues that could arise when designing a heuristic. There are many ways of enhancing the implementation of a given heuristic so to improve its efficiency. Some key items that I found to be useful will be presented. Other related issues that deal with fuzzy logic and multi-objective optimisation are also discussed.

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References

  • Adenso-Diaz, B., & Laguna, B. (2006). Fine tuning of the algorithms using fractional experimental designs and local search. Operations Research, 54, 99–114.

    Article  Google Scholar 

  • Bader, J., & Zitzler, E. (2010). HypE: An algorithm for fast hypervolume-based many objective optimization. Evolutionary Computation, 19, 45–76.

    Article  Google Scholar 

  • Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2002). A fast elitist non-dominated sorting genetic algorithms for multi-objective optimization: NSGA-II. IEEE Transactions on Evolutionary Computation, 6, 182–197.

    Article  Google Scholar 

  • Dorigo, M., Caro, G., & Gambardella, L. (1999). Ant algorithms for discrete optimization. Artificial Life, 5, 137–172.

    Article  Google Scholar 

  • Gendreau, M., Hertz, A., & Laporte, G. (1994). A tabu search heuristic for the vehicle routing problem. Management Science, 40, 1276–1290.

    Article  Google Scholar 

  • Grefenstette, J. J. (1986). Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, 16, 122–128.

    Article  Google Scholar 

  • Hopfield, J., & Tank, D. (1985). Neural computation of decisions in optimization problems. Biological Cybernetics, 52, 141–152.

    Google Scholar 

  • Irawan, C. A., Salhi, S., & Drezner, Z. (2016). Hybrid metaheuristics with VNS and exact methods: Application to large unconditional and conditional vertex p-centre problems. Journal of Heuristics, 22, 507–537.

    Article  Google Scholar 

  • Kahraman, C. (2008). Fuzzy multi-criteria decision making: Theory and applications with recent developments. London: Springer.

    Book  Google Scholar 

  • Kelly, J. P., Golden, B., & Assad, A. A. (1993). Large-scale controlled rounding using tabu search with strategic oscillation. Annals of Operations Research, 41, 69–84.

    Article  Google Scholar 

  • NVIDIA, C. (2007). The open unified device architecture programming guide. Santa Clara: NVIDIA.

    Google Scholar 

  • Osman, I. H. (1993). Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem. Annals of Operations Research, 41, 421–451.

    Article  Google Scholar 

  • Osman, I. H., & Salhi, S. (1996). Local search strategies for the vehicle fleet mix problem. In V. J. Rayward-Smith, I. H. Osman, C. R. Reeves, & G. D. Smith (Eds.), Modern heuristic search techniques (pp. 131–154). New York: Wiley.

    Google Scholar 

  • Pareto, V. (1896). Cours d’Economie Politique: I and II, F. Rouge, Lausanne.

    Google Scholar 

  • Parhani, B. (2006). Introduction to parallel processing algorithms and architectures. London: Springer Science and Business Media.

    Google Scholar 

  • Pospichal, P., Jaros, J., & Schwarz, J. (2010). Parallel genetic algorithm on the CUDA architecture. In C. Di-Chio et al. (Eds.), EvoApplications 2010 (Part I, Lecture notes in computer science, Vol. 6024, pp. 442–451). Berlin: Springer.

    Google Scholar 

  • Rumelhart, D., & McClelland, J. (1986). Parallel distributed processing. Cambridge, MA: MIT Press.

    Google Scholar 

  • Salhi, S. (2002). Defining tabu list size and aspiration criterion within tabu search methods. Computers and Operations Research, 29, 67–86.

    Article  Google Scholar 

  • Salhi, S., & Sari, M. (1997). A Multi-level composite heuristic for the multi-depot vehicle fleet mix problem. European Journal of Operational Research, 103, 78–95.

    Article  Google Scholar 

  • Salhi, S., & Irawan, C. A. (2015). A quadtree-based allocaltion method for a class of large discrete Euclidean location problems. Computers and Operations Research, 55, 23–35.

    Article  Google Scholar 

  • Smithies, R., Salhi, S., & Queen, N. (2004). Adaptive hybrid learning for neural networks. Neural Computation, 16, 139–157.

    Article  Google Scholar 

  • Sze, J. F., Salhi, S., & Wassan, N. (2016). A hybridisation of adaptive variable neighbourhood search and large neighbourhood search: Application to the vehicle routing problem. Expert Systems with Applications, 65, 383–397.

    Article  Google Scholar 

  • Toth, P., & Vigo, D. (2003). The granular tabu search and its applications to the vehicle routing problem. Informs Journal on Computing, 15, 333–346.

    Article  Google Scholar 

  • Woodruff, D. L., & Zemel, E. (1993). Hashing vectors for tabu search. Annals of Operations Research, 41, 123–137.

    Article  Google Scholar 

  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.

    Article  Google Scholar 

  • Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 3, 257–271.

    Article  Google Scholar 

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Authors and Affiliations

  1. Centre for Logistics & Heuristic Optimisation, Kent Business School, University of Kent, Canterbury, UK

    Saïd Salhi

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  1. Saïd Salhi
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Salhi, S. (2017). Implementation Issues. In: Heuristic Search. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-49355-8_6

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