A hybrid evolutionary algorithm for bi-objective job shop scheduling problems

Aiming at solving bi-objective job shop scheduling problems, a hybrid evolutionary algorithm is proposed. An improved elite duplication strategy is applied, which reduces computational cost of the algorithm. An escalating evolutionary strategy is introduced into the algorithm, which is designed to overcome premature convergence. Besides, by applying a variable neighborhood search strategy to achieve Pareto solutions during the population escalation, the algorithm's local search ability is enhanced. Numerical experiments, which employ the proposed algorithm, together with other two typical algorithms NSGA-II and MOGLS, is made to solve 82 bi-objective job shop scheduling problems. The optimization results show the effectiveness of the algorithm proposed here on solving bi-objective job shop scheduling problems.