应用蜜蜂繁殖进化型粒子群算法求解车辆路径问题

为了提高粒子群算法求解车辆路径问题时收敛速度和全局搜索能力,将蜜蜂繁殖进化机制与粒子群算法相结合，应用到CVRP问题的求解。该算法中，最优的个体作为蜂王与通过选择机制选择的雄蜂以随机概率进行交叉，增强了最优个体信息的应用能力;同时，随机产生一部分雄蜂种群，并将其与蜂王交叉增加了算法的多样性。实例分析表明该算法具有较好的全局搜索能力，验证了该算法的可行性。

A Bee Evolutionary Particle Swarm Optimization Algorithm for Vehicle Routing Problem

The vehicle routing problem(VRP) is discussed in this paper.There are studies that solve VRP by using particle swarm optimization(PSO) algorithm.However,with traditional PSO,it has slow convergence rate and a local optimum may be obtained.In order to improve the performance of PSO,an algorithm called bee evolutionary particle swarm optimization(BEPSO) is presented for VRP in this paper.By this algorithm,the best particle regarded as the queen crosses with the selected drones randomly.In this way,it takes the advantage of the best individual’s information.At the same time,some drones are randomly generated and crossed with the queen such that diversity is enlarged.Experimental test shows that the proposed algorithm has better global search ability than the existing ones.