中文摘要：

本文提出了一种基于分布估计的离散粒子群优化算法.提出的新算法突破了传统粒子群速度-位移搜索模型的局限,且种群中的每个粒子具有更全面的学习能力,从而能够有效地解决组合优化问题.仿真实验结果表明提出的新算法的性能优于现有的其它几种离散粒子群优化算法.

英文摘要：

The philosophy behind the original particle swarm optimization（PSO）is to learn from individual＇s own experience and the best individual＇s experience in the whole swarm. Estimation of distribution algorithms（EDAs）generate new solutions from a probability model which characterizes the distribution of the current promising solutions in the search space.A novel discrete particle swarm optimization algorithm based on estimation of distribution（EDPSO）is proposed by reasonably combining the ideas of PSO and EDAs. The proposed algorithm breaks the confine of the original speed and location model, and each particle in the population have comprehensive learning ability. Therefore the proposed algorithm effectively extends the PSO to solve combinatorial optimization problems. Simulation results show that the proposed algorithm has superior performance to other discrete PSOs.