Evolutionary ant colony algorithm using firefly-based transition for solving vehicle routing problems Online publication date: Wed, 11-Mar-2020
by Rajeev Kumar Goel; Raman Maini
International Journal of Computational Science and Engineering (IJCSE), Vol. 21, No. 2, 2020
Abstract: In this paper, we propose an evolutionary optimisation algorithm which adapts the advantages of ant colony optimisation, firefly optimisation algorithms and simulated annealing to solve vehicle routing problem and its variants. Firefly optimisation (FA) along with simulated annealing tries to avoid local optima stagnation of ant colony optimisation. Whereas multi-modal nature of FA helps in exploring the search space, pheromone shaking avoids the stagnation of pheromone deposit on the exploited paths. This is expected to improve the working of ant colony system (ACS). Performance of the proposed algorithm has been compared with the performance of some of other available meta-heuristic approaches currently being used for solving vehicle routing problems on some benchmark problems. Results show the consistency of the proposed approach. Moreover, its convergence rate is also faster and the obtained solutions are closer to optimal as compared to solutions obtained using certain other existing meta-heuristic approaches in use. The results also demonstrate the effectiveness of the presented algorithm over other existing FA-based algorithms for solving vehicle routing problems.
Online publication date: Wed, 11-Mar-2020
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