Abstract
Artificial Bee Colony algorithm is a very powerful Swarm Intelligence Algorithm that has been applied in a number of different kind of optimization problems since the time that it was published. In recent years there is a growing number of optimization models that trying to reduce the energy consumption in routing problems. In this paper, a new variant of Artificial Bee Colony algorithm, the Parallel Multi-Start Multiobjective Artificial Bee Colony algorithm (PMS-ABC) is proposed for the solution of a Vehicle Routing Problem variant, the Multiobjective Energy Reduction Multi-Depot Vehicle Routing Problem (MERMDVRP). In the formulation four different scenarios are proposed where the distances between the customers and the depots are either symmetric or asymmetric and the customers have either demand or pickup. The algorithm is compared with three other multiobjective algorithms, the Parallel Multi-Start Non-dominated Sorting Differential Evolution (PMS-NSDE), the Parallel Multi-Start Non-dominated Sorting Particle Swarm Optimization (PMS-NSPSO) and the Parallel Multi-Start Non-dominated Sorting Genetic Algorithm II (PMS-NSGA II) in a number of benchmark instances.
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Rapanaki, E., Psychas, ID., Marinaki, M., Marinakis, Y. (2020). An Artificial Bee Colony Algorithm for the Multiobjective Energy Reduction Multi-Depot Vehicle Routing Problem. In: Matsatsinis, N., Marinakis, Y., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2019. Lecture Notes in Computer Science(), vol 11968. Springer, Cham. https://doi.org/10.1007/978-3-030-38629-0_17
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