随机交叉粒子群优化算法

Stochastic crossover Particle Swarm Optimization

针对粒子群优化算法容易陷入局部极值点、进化后期收敛慢和优化精度较差等缺点,设计了一种随机交叉算子,提出了随机交叉粒子群优化算法。该算法在每次迭代中,对当前粒子和整个粒子群的最优粒子进行随机交叉,产生新的较优粒子并代替原来的粒子,从而加快了算法的收敛速度,增强了算法的寻优能力。仿真结果表明,该算法具有较高的优化性能。

A stochastic crossover particle swarm optimization algorithm is proposed by designing a stochastic cross-operator,which aims at the disadvantages of PSO such as the easily falling into local extremum point,slow convergence.In each iteration of this algorithm,the current particles and the optimal particle of the particle swarm make a stochastic crossover,and form new particles of good quality,which will substitute the former particles,so it advances the speed of convergence of the algorithm,and enhances the optimization capacity of the algorithm.The results illustrate this algorithm has higher optimization performance.