Hybrid evolutionary algorithm for multi-objective job shop scheduling

In this paper, a hybrid quantum-inspired evolutionary algorithm (HQEA) for multi-objective JSSP is proposed. In the HQEA, a quantum bit is employed to represent processing priority of two operations executed on the same machine. Updating operator of quantum gate is used to speed up individuals converge toward the current best solution. Conventional crossover is performed as well. However, an individual produced by updating operator and crossover operator may represent no feasible schedule. To repair illegal solution, harmonization algorithm is employed. At last, local search operator is designed to exploit the space around the current best solution. Experiments are conducted on benchmark test problems, the results show that the proposed approach can search for the near-optimal and non dominated solutions by optimizing the makespan and mean flow time. The results of comparisons demonstrates that the proposed approach outperform another well established multi-objective evolutionary algorithm based JSSP approach.