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Influenza Virus Algorithm for Multiobjective Energy Reduction Open Vehicle Routing Problem

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Part of the Emergence, Complexity and Computation book series (ECC,volume 32)

Abstract

We propose a Parallel Multi-Start Multiobjective Influenza Virus Algorithm (PMS-MOIVA) for the solution of the Multiobjective Energy Reduction Open Vehicle Routing Problem. The PMS-MOIVA could be categorized in the Artificial Immune System algorithms, as it simulates the process of annual evolution of influenza virus in an isolated human population. Two different versions of the algorithm are presented where their main difference is the fact that in the first version, PMS-MOIVA1, the algorithm focuses on the improvement of the most effective solutions using a local search procedure while in the second version, PMS-MOIVA2, the use of the local search procedure is applied equally in the whole population. In order to prove the effectiveness of the proposed algorithm a comparison is performed with the Parallel Multi-Start Non-dominated Sorting Genetic Algorithm II (PMS-NSGA II). The Multiobjective Energy Reduction Open Vehicle Routing Problem has two different objective functions, the first corresponds to the optimization of the total travel time and the second corresponds to the minimization of the fuel consumption of the vehicle taking into account the travel distance and the load of the vehicle when the decision maker plans delivery. A number of modified Vehicle Routing Problem instances are used in order to evaluate the quality of the proposed algorithm.

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Correspondence to Iraklis-Dimitrios Psychas .

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Psychas, ID., Delimpasi, E., Marinaki, M., Marinakis, Y. (2018). Influenza Virus Algorithm for Multiobjective Energy Reduction Open Vehicle Routing Problem. In: Adamatzky, A. (eds) Shortest Path Solvers. From Software to Wetware. Emergence, Complexity and Computation, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-319-77510-4_5

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  • DOI: https://doi.org/10.1007/978-3-319-77510-4_5

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