摘要
分析粒子群算法在求解组合优化问题中的运行原理,对警车分布的优化问题建立了粒子群优化的数学模型,对基本粒子群优化算法中的速度范围、惯性权重等参数进行了改进,并通过仿真与基本粒子群算法比较,显示改进的粒子群算法,提高了优化结果.在改进的粒子群算法中引入遗传算法,将形成的新混合算法应用到求解警车最优执勤地点的分布问题,并与遗传算法和改进的粒子群算法仿真比较.结果表明,混合优化算法在收敛速度和精度上均有明显的提高.
The mechanism of particle swarm optimization （PSO） in solving combinatorial optimization problems was analyzed. A mathematical model of PSO algorithm for optimization of police cars distribution was set up. Parameters of PSO, such as speed range and inertia weight, were improved. Comparing improved PSO with original PSO, the simulation shows that improved PSO can produce a better optimization result. Then genetic algorithm （GA） was introduced into improved PSO to form a new hybrid algorithm which can be used to decide the optimization distribution of police cars. Simulations comparison with GA and improved PSO shows that the hybrid optimization algorithm has obvious improvements in convergence speed and accuracy.
出处
《上海工程技术大学学报》
CAS
2011年第3期262-265,共4页
Journal of Shanghai University of Engineering Science
关键词
适应度函数
粒子群优化算法
遗传算法
警车分布
fitness function
PSO（Particle Swarm Optimization） algorithm
genetic algorithm（GA）
police cars distribution
