Abstract
Krill Herd algorithm is a powerful and relatively new 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 are trying to reduce the energy consumption in routing problems. In this paper, a new variant of Krill Herd algorithm, the Parallel Multi-Start Non-dominated Sorting Krill Herd algorithm (PMS-KH), is proposed for the solution of a Vehicle Routing Problem variant, the Multiobjective Energy Reduction Multi-Depot Vehicle Routing Problem (MERMDVRP). Four different models are proposed where the distances between the customers and between the customers and the depots are either symmetric or asymmetric and the customers have either demand or pickup. The algorithm is compared with four other multiobjective algorithms, the Parallel Multi-Start Non-dominated Sorting Artificial Bee Colony (PMS-ABC), 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, giving very satisfactory results.
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Rapanaki, E., Psychas, I..D., Marinaki, M., Matsatsinis, N., Marinakis, Y. (2020). A Krill Herd Algorithm for the Multiobjective Energy Reduction Multi-Depot Vehicle Routing Problem. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12565. Springer, Cham. https://doi.org/10.1007/978-3-030-64583-0_39
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