Abstract
In this chapter we propose two trajectory-based heuristics, particularly Simulated Annealing and Tabu Search, to solve instances of the Internet Shopping Optimisation Problem (ISOP). Since these metaheuristics are relatively less costly in terms of computational resources such as CPU time and memory, compared to population-based metaheuristics, but with a better performance than simpler heuristics, its use in the context of internet shopping is relevant. In such context the user of the service expects good quality solutions, while the provider wants to maintain the use of resources at a low level.
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Acknowledgments
This research project has been partly financed by CONACyT, COTACyT, the Tecnológico Nacional de México campus Instituto Tecnológico de Ciudad Madero, and the University of Luxembourg.
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López-Locés, M.C., Rege, K., Pecero, J.E., Bouvry, P., Fraire Huacuja, H.J. (2015). Trajectory Metaheuristics for the Internet Shopping Optimization Problem. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization. Studies in Computational Intelligence, vol 601. Springer, Cham. https://doi.org/10.1007/978-3-319-17747-2_41
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DOI: https://doi.org/10.1007/978-3-319-17747-2_41
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