改进免疫粒子群算法在矿车调度优化中的应用

Application of Improved Immune Particle Swarm Algorithm in Vehicle Scheduling Optimization

提出了一种基于自适应搜索的改进免疫粒子群算法.算法在传统免疫粒子群算法的基础上,对子种群进行分组,以并联形式对算法进行融合,动态调整各组子种群规模,根据粒子最大浓度值自适应调整搜索范围.首先,算法融合了浓度调节机制,结合粒子最大浓度值来调节子种群数目以充分利用粒子群资源;与此同时,针对次优子种群进行疫苗接种,利用粒子最大浓度值调节接种疫苗的搜索范围,在避免了种群退化现象的同时,提高了算法的收敛精度和全局搜索能力.文中建立了露天矿山矿车调度模型并进行了仿真实验,仿真结果表明,所提算法充分利用了矿车资源,具有一定优越性和较好的工程应用价值.

An immune particle swarm algorithm based on adaptive search strategy is proposed in this paper. Based on the traditional immune particle swarm algorithm, the sub populations are grouped on the fusion algorithm in parallel form, the size of each group is adjusted dynamically, and the search range is also adjusted, according to the maximum concentration of particles. Firstly, combing with the adjustment mechanism of concentration and the maximum value of concentration, the algorithm adjusts the number of sub populations, in order to make full use of the particle source. At the same time, the inferior sub-populations are vaccinated, and the maximum concentration of the particles is used to control the search range of the vaccine. Avoiding the degradation of population, the convergence accuracy and the global search ability of the algorithm are improved. A vehicle scheduling model of open-pit mine is established and simulation experiments are carried out. The simulation results show the proposed algorithm makes full use of the tramcar source, and has certain advantage and good engineering application value.