Hybrid Fruit Fly Algorithm Based on Bi-objective Job-shop Scheduling

Abstract

Job-shop scheduling is one of the most important problems in workshop scheduling and is significant for improving production efficiency. This paper proposes a hybrid fruit fly algorithm based on bi-objective job-shop scheduling. Based on the existing fruit fly optimization algorithm, the paper presents a hybrid step-size olfactory search method to improve search efficiency. It uses the global collaboration mechanism to increase diversity and cooperation of the fruit fly population, to avoid falling into the local optimum and escape premature convergence, and to make the algorithm have more opportunities to jump away from the local extrema. This paper proposes the external essence of a library evolution strategy based on the crowding-distance approach to objectively determine the multi-objective fitness value, and strategically guide the hybrid fruit fly algorithm to evolve to the Pareto fronts. The simulation results show that the algorithm is simple and has strong global optimization ability for effectively solving the bi-objective job-shop scheduling problem.