Particle swarm optimization of corro-factor andbilingual learning mechanism
一种带交叉因子的双向寻优粒子群优化算法

To overcome the disadvantages of particle swarm optimization PSO algorithm such as premature, bad convergence rate, this paper presented an improved algorithm CBMPSO. The algorithm first made the initial population in the searching space evenly distributed, then calculated the initial and the opposite ones' fitness value, chose the better ones as the initial population. Added global poorest position to the update of particle position and started corro-factor and bilingual learning Mechanism randomly. The numeric experiments indicate that the new strategy can not only speed up the convergence but also can avoid the premature convergence problem effectively.