THE SEARCH OF BALANCE BETWEEN DIVERSIFICATION AND INTENSIFICATION IN ARTIFICIAL BEE COLONY ALGORITHM TO SOLVE JOB SHOP SCHEDULING PROBLEM

摘要

Several intelligent theories have been proposed to solve job shop scheduling problems with the target of minimizing makespan such as hybrid metaheuristics. The core advantage of hybridization is to improve the effectiveness of the algorithm leading to better quality of solutions. The paper establishes seven hybrid methods namely HABCGA according to sixteen configurations (four crossover operators: PMX, OX, CX and PBX, and four mutation operators: swap, inversion, insertion and displacement) to solve job shop scheduling problems with the objective of minimizing makespan and to test these algorithms on 250 Benchmark instances to evaluate the performance and to find the balance that can lead to the optimal performance of the hybrid ABC. The results indicate how the multiple hybridizations, the crossover operator type, and the mutation operator type affect in a positive way the balance between diversification and intensification in artificial bee colony algorithm to solve job shop scheduling problem.