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Cooperative Parallel Metaheuristics based Penguin Optimization Search for Solving the Vehicle Routing Problem

Cooperative Parallel Metaheuristics based Penguin Optimization Search for Solving the Vehicle Routing Problem

Meryem Ammi, Salim Chikhi
Copyright: © 2016 |Volume: 7 |Issue: 1 |Pages: 18
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781466691209|DOI: 10.4018/IJAMC.2016010101
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MLA

Ammi, Meryem, and Salim Chikhi. "Cooperative Parallel Metaheuristics based Penguin Optimization Search for Solving the Vehicle Routing Problem." IJAMC vol.7, no.1 2016: pp.1-18. http://doi.org/10.4018/IJAMC.2016010101

APA

Ammi, M. & Chikhi, S. (2016). Cooperative Parallel Metaheuristics based Penguin Optimization Search for Solving the Vehicle Routing Problem. International Journal of Applied Metaheuristic Computing (IJAMC), 7(1), 1-18. http://doi.org/10.4018/IJAMC.2016010101

Chicago

Ammi, Meryem, and Salim Chikhi. "Cooperative Parallel Metaheuristics based Penguin Optimization Search for Solving the Vehicle Routing Problem," International Journal of Applied Metaheuristic Computing (IJAMC) 7, no.1: 1-18. http://doi.org/10.4018/IJAMC.2016010101

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Abstract

In this work the authors present a new approach based on the cooperation of many variants of metaheuristics in order to solve the large existing benchmark instances of the capacitated vehicle routing problem (CVRP). The proposed method follows the parallel pattern of the generalized island model (GIM). Consequently, the used metaheuristics, namely genetic algorithm (GA), the ant colony optimization (ACO), and the first application of the penguin optimization search (PEO) have been used to handle the large size of the CVRP. These optimization processes have been put over numerous islands that communicate via the process of exchanging solutions. Comparative studies as well as tests over the existing benchmark instances have been reported to prove the efficiency of the proposed approach.

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