【摘 要】
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To improve the optimization ability of particle swarm optimization,we propose an improved particle swarm optimization algorithm and apply it to solve vehicle routing problem.The adaptive particle swar
【机 构】
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Chang'an University,School of Automobile Chang'
论文部分内容阅读
To improve the optimization ability of particle swarm optimization,we propose an improved particle swarm optimization algorithm and apply it to solve vehicle routing problem.The adaptive particle swarm optimization algorithm based on the combination of linear and nonlinear inertia weight is helpful for the local search in the pre-stage and the global search in the later stage.Combined with simulated annealing algorithm,the improved particle swarm optimization algorithm can avoid the local optimum and the deviation from the optimal solution.In this paper,the vehicle path model is constructed,and the improved particle swarm optimization algorithm is used to optimize the vehicle routing problem.Compared with the genetic algorithm and the improved particle swarm optimization algorithm,the results show that the improved particle swarm optimization algorithm can quickly and effectively get optimal solution to the vehicle routing problem,demonstrating the improved particle swarm optimization algorithm is a more effective method to solve the vehicle routing problem.
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