Abstract
Artificial fish swarm algorithm (AFSA) has important theoretical research value and practical significance in solving VRP. The traditional AFSA which does not consider the structural features of VRP will lead to too complex for the process to solve problems, too much time to search optimal solution and too low computational accuracy. In this paper, the traditional method is improved that neighborhood search which are more efficient for VRP are used in the three behaviors of AF swarm, and discretize the three behaviors. The improvement optimizes the behavior of finding optimal solution of AFSA in VRP, and avoids the convergence rate becoming too fast in later stage and falling into the local optimal while expanding the search. Through the experimental comparative analysis, the improved method is more effective and feasible than traditional method.
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Acknowledgements
The work was supported by the Special Scientific Research Fund of Food Public Welfare Profession of China (201513004-3), subproject of the National Key Research and Development Program of China (2017YFD0401102-02), the Guiding Scientific Research Project of Hubei Provincial Education Department (B2017078) and the Humanities and Social Sciences Fund Project of Hubei Provincial Education Department (17Y071).
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Jia, S. et al. (2018). Application of Artificial Fish Swarm Algorithm in Vehicle Routing Problem. In: Qiao, J., et al. Bio-inspired Computing: Theories and Applications. BIC-TA 2018. Communications in Computer and Information Science, vol 952. Springer, Singapore. https://doi.org/10.1007/978-981-13-2829-9_1
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DOI: https://doi.org/10.1007/978-981-13-2829-9_1
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