Fast Multi-objective Hybrid Evolutionary Algorithm for Flow Shop Scheduling Problem

In this paper, a fast multi-objective hybrid evolutionary algorithm (MOHEA) is proposed to solve the bi-criteria flow shop scheduling problem with the objectives of minimizing makespan and total flow time. The proposed algorithm improves the vector evaluated genetic algorithm (VEGA) by combing a new sampling strategy according to the Pareto dominating and dominated relationship-based fitness function. VEGA is good at searching the edge region of the Pareto front, but it has neglected the central area of the Pareto front, and the new sampling strategy prefers the center region of the Pareto front. The hybrid sampling strategy improves the convergence performance and the distribution performance. Simulation experiments on multi-objective test problems show that, compared with NSGA-II and SPEA2, the fast multi-objective hybrid evolutionary algorithm is better in the two aspects of convergence and distribution, and has obvious advantages in the efficiency.