基于细菌觅食机理改进粒子群算法的研究

摘要

粒子群算法与细菌觅食算法在优化问题中均体现了较好的性能,但由于各自特定的进化机制,也都存在缺点.粒子群优化( PSO)算法在优化过程中过快陷入局部极值,为了避免这个缺陷,提出了一种新的混合算法.通过PSO算法完成整个空间的全局搜索,通过细菌觅食算法(BFOA)中的趋向性运动算子完成局部搜索的功能,再通过典型函数进行测试,结果表明新算法可以有效弥补细菌觅食算法速度不快和粒子群算法精度不高的缺陷,同时部分地避免了局部收敛的问题,从而适用于解决复杂函数的优化问题.%Particle swarm optimization algorithm and bacterial foraging optimization are all reflect the good performance in the optimization problems, while they all have shortcomings. Because of their particular evolutionary mechanism. To avoid fall into the local minimum in the standard partical swarm optimization (PSO ) algorithm, this paper proposed a new algorithm. The new algorithm completes the global search space through the PSO operator and completes local search space by the trend movement of Bacteria foraging(BFO) algorithm. Tests show that the new algorithm can effectively compensate for the slow pace of bacterial foraging algorithm and shortcoming that particle swarm algorithm's accuracy is not high, while avoiding some of the local convergence problems, Thus applicable to solving complex function optimization problems.