摘要： 提出一种基于文化算法的粒子群优化算法(PSO)。该算法在群体空间采用基于高斯概率分布和柯西概率分布的改进PSO算法，在信念空间根据形势知识和规范化知识指导种群的进化，充分利用优秀个体所包含的信息，提高了算法的进化速度。实验表明，该算法的优化性能和效率优于基本PSO算法。

关键词: 粒子群优化算法, 文化算法, 约束优化问题

Abstract: A Particle Swarm Optimization(PSO) based on cultural algorithm for solving constrained optimization problems is proposed. This algorithm employs PSO using Gaussian and Cauchy probability distributions in population space, uses situational knowledge and normative knowledge in belief space to guide the evolution of the population. In this way, it exploits the information sufficiently that the optimum individual carries and speeds up the evolutionary process. Experimental results prove the algorithm is superior to basic PSO in quality and efficiency.

Key words: particle swarm optimization, cultural algorithm, constrained optimization

中图分类号: 

• TP301.6