An Improved Particle Swarm Optimization for Permutation Flowshop Scheduling Problem with Total Flowtime Criterion

This paper deals with the m-machine permutation flowshop scheduling problem to minimize the total flowtime, an NP-complete problem, and proposes an improved particle swarm optimization (PSO) algorithm. To enhance the exploitation ability of PSO, a stochastic iterated local search is incorporated. To improve the exploration ability of PSO, a population update method is applied to replace non-promising particles. In addition, a solution pool that stores elite solutions found in the search history is adopted, and in the evolution process each particle learns from this solution pool besides its personal best solution and the global best solution so as to improve the learning capability of the particles. Experimental results on benchmark instances show that the proposed PSO algorithm is competitive with other metaheuristics.