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Evolutionary Ant Colony Algorithm Using Firefly Based Transition for Solving Vehicle Routing Problems: EAFA for VRPs

Evolutionary Ant Colony Algorithm Using Firefly Based Transition for Solving Vehicle Routing Problems: EAFA for VRPs

Rajeev Goel, Raman Maini
Copyright: © 2019 |Volume: 10 |Issue: 3 |Pages: 15
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781522566380|DOI: 10.4018/IJSIR.2019070103
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MLA

Goel, Rajeev, and Raman Maini. "Evolutionary Ant Colony Algorithm Using Firefly Based Transition for Solving Vehicle Routing Problems: EAFA for VRPs." IJSIR vol.10, no.3 2019: pp.46-60. http://doi.org/10.4018/IJSIR.2019070103

APA

Goel, R. & Maini, R. (2019). Evolutionary Ant Colony Algorithm Using Firefly Based Transition for Solving Vehicle Routing Problems: EAFA for VRPs. International Journal of Swarm Intelligence Research (IJSIR), 10(3), 46-60. http://doi.org/10.4018/IJSIR.2019070103

Chicago

Goel, Rajeev, and Raman Maini. "Evolutionary Ant Colony Algorithm Using Firefly Based Transition for Solving Vehicle Routing Problems: EAFA for VRPs," International Journal of Swarm Intelligence Research (IJSIR) 10, no.3: 46-60. http://doi.org/10.4018/IJSIR.2019070103

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Abstract

Vehicle routing problems are a classical NP-hard optimization problem. In this article we propose an evolutionary optimization algorithm which adapts the advantages of ant colony optimization and firefly optimization to solve vehicle routing problem and its variants. Firefly optimization (FA) based transition rules and a novel pheromone shaking rule is proposed to escape local optima. Whereas the multi-modal nature of FA explores the search space, pheromone shaking avoids the stagnation of pheromones on the exploited paths. This is expected to improve working of an ant colony system (ACS). Performance of the proposed algorithm is compared with the performance of some of other currently available meta-heuristic approaches for solving vehicle routing problems (VRP) by applying it to certain standard benchmark datasets. Results show that the proposed approach is consistent and its convergence rate is faster. The results also demonstrate the superiority of the proposed approach over some of the other existing FA-based approaches for solving such type of discrete optimization problems.

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