Particle swarm optimization algorithm (PSO) and estimation of distribution algorithm (EDA) are seldom applied to permutation-based combinatorial optimization problems.This paper presents an estimation of distribution-discrete particle swarm optimization algorithm (ED-DPSO) for the permutation-based problems.In ED-DPSO

one part of components of the offspring comes from the longest common subsequence between the current solution and the global best solution

and the other part comes from the probability model built on the distribution information of all personal best solutions.In ED-DPSO

the current solution

all personal best solutions and global best solution contribute to the generation of a new solution.Thus

ED-PSO has more comprehensive learning ability

and can avoid falling into local minima and improve the search ability.Experiment results on two classic permutation-based problems show ED-PSO has superior performance.