Hybrid particle-swarm optimization for multi-objective flexible job-shop scheduling problem

Flexible job-shop scheduling is a very important branch in both fields of production management and com-binatorial optimization.A hybrid particle-swarm optimization algorithm is proposed to study the mutli-objective flexible job-shop scheduling problem based on Pareto-dominance.First,particles are represented based on job operation and ma-chine assignment,and are updated directly in the discrete domain.Then,a multi-objective local search strategy including Baldwinian learning mechanism and simulated annealing technology is introduced to balance global exploration and local exploitation.Third,Pareto-dominance is applied to compare different solutions,and an external archive is employed to hold and update the obtained non-dominated solutions.Finally,the proposed algorithm is simulated on numerical clas-sical benchmark examples and compared with existing methods.It is shown that the proposed method achieves better performance in both convergence and diversity.