Abstract: For the disadvantages of Particle Swarm Optimization（PSO） algorithm, such as “premature”, slow convergence speed and low convergence precision in the late evolutionary, a novel hybrid PSO algorithm is proposed. The algorithm applies the entropy theory to generate the initial population, and integrates simulated annealing and the ideas of hybridization and mutation in the genetic algorithm into evolutionary process. Results of simulation experiment show that the algorithm compared with other modified PSO algorithms proposed before is improved virtually on anti “premature” ability, optimization precision and stability.

Key words: hybrid algorithm, Particle Swarm Optimization（PSO）, intelligent optimization, entropy

摘要： 针对粒子群算法的“早熟”，进化后期收敛速度慢及精度低等问题，提出了一种改进的PSO算法。为保证初始群体的遍历性，改进算法首先利用了信息熵产生初始群体；为提高进化过程中群体的多样性，将遗传算法中杂交、变异的思想融入了算法中；为提高算法晚期的收敛速度，将模拟退火算法中退火的思想引入到杂交过程中。该算法与其他改进算法进行数值比较，仿真实验表明，提出的算法抗“早熟”能力强，搜索精度高，稳定性好。

关键词: 混合算法, 粒子群, 智能优化, 信息熵