An improved differential evolution for permutation flowshop scheduling problem with total flowtime criterion

Abstract

This paper proposes an improved differential evolution (DE) for the permutation flowshop scheduling problem with the total flowtime minimization, an NP-complete problem. To enhance the exploration ability of DE, a hybrid method of simulated annealing and stochastic variable neighborhood search are incorporated. To improve the search diversification of DE, a population restart method based path relinking is applied to replace non-promising solutions. Experimental results on benchmark instances show that the proposed DE algorithm is competitive to other metaheuristics proposed for the PFSP with total flowtime minimization in the literature.