基于细菌觅食与粒子群的改进混合算法

针对粒子群优化算法（PSO）在优化过程中易陷入局部极值而产生“早熟”现象，文中提出一种基于细菌觅食与粒子群的改进混合算法。粒子群优化算法与细菌觅食优化算法的结合，增强了算法的全局搜索能力，使算法具有全局搜索能力强的优点。选用Matlab进行仿真实验，实验结果进一步显示了改进混合算法的优化能力优于基本PSO算法和基本BFO算法，收敛速度快，且具有较好的鲁棒性。

Improved Hybrid Algorithm Based on Bacterial Foraging and Particle Swarm Optimization

Aiming at the phenomenon that the particle swarm optimization algorithm is easy to fall into the local extremum in the optimization process, an improved hybrid algorithm based on bacterial foraging and particle swarm optimization is proposed. Particle swarm optimization algorithm and the bacterial foraging optimization algorithm combined, the enhanced algorithm Alto global searching ability. The algorithm has the advantages of strong global search ability. Matlab simulation experiment, the results show that the improved hybrid algorithm is better than the basic PSO algorithm and the basic BFO algorithm, the convergence rate is fast, and has good robustness.