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分析将蚁群优化算法应用于预防性维修周期工程寻优问题时遇到的算法参数选择困难等问题,提出将粒子群优化算法和空间划分方法引入该过程以改进原蚁群算法的寻优规则和历程.建立混合粒子群和蚁群算法的群智能优化策略:PS_ACO(Particle Swarm and Ant Colony Optimization),并将其应用于混联系统预防性维修周期优化过程中,以解决由于蚁群算法中参数选择不当和随机产生维修周期解值带来的求解精度差、寻优效率低等问题.算法的寻优结果对比分析表明:该PS_ACO算法应用于预防性维修周期优化问题,在寻优效率及寻优精度上有部分改进,且可相对削弱算法参数选择对优化结果的影响.
This paper analyzes the problems that the ant colony optimization algorithm is applied to the problem of algorithm parameter selection encountered in the preventive maintenance cycle engineering optimization problem and proposes to introduce the particle swarm optimization algorithm and the space partitioning method into the process to improve the optimization rules of the original ant colony algorithm And its history.Group Swarm Optimization (PSO) and Ant Colony Optimization (PSO), which is based on Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), are developed and applied to the preventive maintenance cycle optimization of hybrid systems. Parameter selection and randomly generated maintenance cycle solution to the problem of poor precision, low optimization efficiency and comparative analysis of the results of the optimization algorithm show that: the PS_ACO algorithm is applied to the preventive maintenance cycle optimization problem, the optimization efficiency and There is some improvement in the optimization accuracy, and the influence of the parameters selection on the optimization results can be relatively weakened.