Computer Science ›› 2020, Vol. 47 ›› Issue (7): 186-191.doi: 10.11896/jsjkx.190600089

• Artificial Intelligence • Previous Articles     Next Articles

Novel Artificial Bee Colony Algorithm for Solving Many-objective Scheduling

ZHENG You-lian1, LEI De-ming2, ZHENG Qiao-xian1   

  1. 1 School of Computer Science and Information Engineering,Hubei University,Wuhan 430062,China
    2 School of Automation,Wuhan University of Technology,Wuhan 430070,China
  • Received:2019-06-18 Online:2020-07-15 Published:2020-07-16
  • About author:ZHENG You-lian,born in 1972,Ph.D,associate professor.Her main research interests include intelligent optimization and scheduling.
    ZHENG Qiao-xian,born in 1978,Ph.D,associate professor.Her main research interests include intelligent algorithm and assembly line scheduling.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61803149)

Abstract: Many-objective continuous optimization problem has been considered extensively while there are few studies on many-objective combination optimization problem.Artificial bee colony(ABC) algorithm has been successfully applied to solve various production scheduling problem,but ABC is seldom used to solve many-objective scheduling problem and many-objective scheduling problem itself is also seldom handled.Aiming at multi-objective flexible job shop scheduling problem,a new ABC algorithm is proposed to optimize simultaneously maximum completion time,total tardiness,total energy consumption and total workload.Unlike the general flexible job shop scheduling problem,the above problem is green scheduling one because of the inclusion of total energy consumption.The new ABC has new characteristics which are obviously different from the existing ABC algorithm.Its number of onlooker bees is less that of employed bees,employed bee focuses on global search while onlooker bee only carries out local search,which avoids the algorithm from falling into local optimization through the different search methods of two kinds of bees.At the same time,onlooker bee just selects some best employed bees or members of external file,and some employed bees cannot become follower objects to avoid wasting computing resources on search for poor solutions.A new strategy is adopted to handle scout.The simulation results show that the ratio of the number of non-dominated solutions to population scale for many-objective scheduling problem is notably less than the same ratio for many-objective continuous optimization problem.Compared with multi-objective genetic algorithm and variable neighborhood search,the computational results show that ABC has better results than two comparative algorithms on solving the considered many-objective scheduling.

Key words: Artificial bee colony, Multi-objective optimization, Scheduling problem, External archive, Local optima

CLC Number: 

  • TP301.6
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