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A Hybrid of Sine Cosine and Particle Swarm Optimization (HSPS) for Solving Heterogeneous Fixed Fleet Vehicle Routing Problem

A Hybrid of Sine Cosine and Particle Swarm Optimization (HSPS) for Solving Heterogeneous Fixed Fleet Vehicle Routing Problem

Sandhya Bansal, Savita Wadhawan
Copyright: © 2021 |Volume: 12 |Issue: 1 |Pages: 25
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799861126|DOI: 10.4018/IJAMC.2021010103
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MLA

Bansal, Sandhya, and Savita Wadhawan. "A Hybrid of Sine Cosine and Particle Swarm Optimization (HSPS) for Solving Heterogeneous Fixed Fleet Vehicle Routing Problem." IJAMC vol.12, no.1 2021: pp.41-65. http://doi.org/10.4018/IJAMC.2021010103

APA

Bansal, S. & Wadhawan, S. (2021). A Hybrid of Sine Cosine and Particle Swarm Optimization (HSPS) for Solving Heterogeneous Fixed Fleet Vehicle Routing Problem. International Journal of Applied Metaheuristic Computing (IJAMC), 12(1), 41-65. http://doi.org/10.4018/IJAMC.2021010103

Chicago

Bansal, Sandhya, and Savita Wadhawan. "A Hybrid of Sine Cosine and Particle Swarm Optimization (HSPS) for Solving Heterogeneous Fixed Fleet Vehicle Routing Problem," International Journal of Applied Metaheuristic Computing (IJAMC) 12, no.1: 41-65. http://doi.org/10.4018/IJAMC.2021010103

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Abstract

Heterogeneous fixed fleet vehicle routing problem is a real-life variant of classical VRP, which is a well-established NP-hard optimization problem. In this paper, a hybrid approach based on sine cosine algorithm and particle swarm optimization, namely HSPS, is proposed to solve heterogeneous vehicle routing problem. This hybridization incorporates the strength of both the algorithms for solving this variant. It works in two stages. In first stage, sine cosine algorithm is used to examine the unexplored solution space, and then in next stage, particle swarm optimization is used to exploit the search space. The proposed algorithm has been tested and compared with other algorithms on several benchmark instances. The numerical and statistical results demonstrate that the proposed hybrid is competitive with other existing hybrid algorithms in solving benchmarks with faster convergence rate.

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