Crowding-measure-based multiobjective evolutionary algorithm for job shop scheduling

Multiobjective evolutionary algorithm (MOEA) has attracted much attention in the past decade; however, the application of MOEA to practical problems such as job shop scheduling is seldom considered. In this paper, crowding-measure-based multiobjective evolutionary algorithm (CMOEA) is first designed, which makes use of the crowding measure to adjust the external population and assign different fitness for individuals; then CMOEA is applied to job shop scheduling to minimize makespan and the total tardiness of jobs. Finally, the comparison between CMOEA and SPEA demonstrates that CMOEA performs well in job shop scheduling.

[1]  J. D. Schaffer,et al.  Multiple Objective Optimization with Vector Evaluated Genetic Algorithms , 1985, ICGA.

[2]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[3]  Chandrasekharan Rajendran,et al.  Scheduling in flowshop and cellular manufacturing systems with multiple objectives— a genetic algorithmic approach , 1996 .

[4]  Hisao Ishibuchi,et al.  A multi-objective genetic local search algorithm and its application to flowshop scheduling , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[5]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[6]  Joshua D. Knowles,et al.  M-PAES: a memetic algorithm for multiobjective optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[7]  Gary B. Lamont,et al.  Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art , 2000, Evolutionary Computation.

[8]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[9]  N. Jawahar,et al.  A multiobjective genetic algorithm for job shop scheduling , 2001 .

[10]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[11]  Pierre Borne,et al.  Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic , 2002, Math. Comput. Simul..

[12]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[13]  David W. Corne,et al.  Properties of an adaptive archiving algorithm for storing nondominated vectors , 2003, IEEE Trans. Evol. Comput..

[14]  Gary G. Yen,et al.  Rank-density-based multiobjective genetic algorithm and benchmark test function study , 2003, IEEE Trans. Evol. Comput..