Abstract
Vehicle routing problem (VRP) is a classic combinatorial optimization problem and has many applications in industry. Solutions of VRP have significant impact on logistic cost. In most VRP models, the shortest distance is used as the objective function, which is not the case in many real-word applications. To this end, a VRP model with fixed and fuel cost is proposed. Genetic algorithm (GA) is a common approach for solving VRP. Due to the premature issue in GA, a tabu bee colony-based GA is employed to solve this model. The improved GA has three characteristics that differentiate from other similar algorithms: (1) The maximum preserved crossover is proposed, to protect the outstanding sub-path and avoid the phenomenon that two identical individuals cannot create new individuals; (2) The bee evolution mechanism is introduced. The optimal solution is selected as the queen-bee and a number of outstanding individuals are as the drones. The utilization of excellent individual characteristics is improved through the crossover of queen-bee and drones; (3) The tabu search is applied to optimize the queen-bee in each generation of bees and improve the quality of excellent individuals. Thus the population quality is improved. Extensive experiments were conducted. The experimental results show the rationality of the model and the validity of the proposed algorithm.
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Acknowledgments
This work is supported by the Fundamental Research Funds for the Central Universities (No. XDJK2016A008), CQ CSTC (No. cstc2015gjhz40002), Chongqing Graduate Student Research Innovation Project (No. CYB16064) and CCF-DiDi bigData Joint Lab. Dr. Chao Gao and Prof. Zili Zhang are the corresponding authors of this paper.
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Lv, L., Liu, Y., Gao, C., Chen, J., Zhang, Z. (2018). Solving Vehicle Routing Problem Through a Tabu Bee Colony-Based Genetic Algorithm. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10941. Springer, Cham. https://doi.org/10.1007/978-3-319-93815-8_19
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DOI: https://doi.org/10.1007/978-3-319-93815-8_19
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