Applying the clonal selection principle to find flexible job-shop schedules

  • Authors:
  • Z. X. Ong;J. C. Tay;C. K. Kwoh

  • Affiliations:
  • Evolutionary and Complex Systems Lab, Nanyang Technological University;Evolutionary and Complex Systems Lab, Nanyang Technological University;Evolutionary and Complex Systems Lab, Nanyang Technological University

  • Venue:
  • ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
  • Year:
  • 2005

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Abstract

We apply the Clonal Selection principle of the human immune system to solve the Flexible Job-Shop Problem with recirculation. Various practical design issues are addressed in the implemented algorithm, ClonaFLEX; first, an efficient antibody representation which creates only feasible solutions and a bootstrapping antibody initialization method to reduce the search time required. Second, the assignment of suitable mutation rates for antibodies based on their affinity. To this end, a simple yet effective visual method of determining the optimal mutation value is proposed. And third, to prevent premature convergence, a novel way of using elite pools to incubate antibodies is presented. Performance results of ClonaFLEX are obtained against benchmark FJSP instances by Kacem and Brandimarte. On average, ClonaFLEX outperforms a cultural evolutionary algorithm (EA) in 7 out of 12 problem sets, equivalent results for 4 and poorer in 1.