Application of the partial enumeration selection method in genetic algorithms to solving a multi-objective flowshop problem

A partial enumeration selection method (PESM) is adopted in a genetic algorithm for solving a multi-objective flowshop problem. Based on the idea of adjusting selection pressure and reinforcing the Pareto front, the PESM-based genetic algorithm balances the exploitation and the exploration. Without time-consuming computation of distance information among individuals and hard-set parameters, this algorithm implicitly maintains diversity in the population. The PESM-based genetic algorithm is implemented and tested on a multi-objective flowshop scheduling problem. In order to compare the solution quality, an out-performance rate measure is proposed to work together with comparison of diversity. Simulation results show that the algorithm proposed improves specific results recently available in the literature and gets smooth nondominated fronts