A multi-modal immune algorithm for the job-shop scheduling problem

This paper describes the application of an artificial immune system to a scheduling application. A novel approach multi-modal immune algorithm is proposed for finding optimal solutions to job-shop scheduling problems emulating the features of a biological immune system. Inter-relationships within the proposed algorithm resemble antibody molecule structure, antibody-antigen relationships in terms of specificity, clonal proliferation, germinal center, and the memory characteristics of adaptive immune responses. Gene fragment recombination and several antibody diversification schemes including somatic recombination, somatic mutation, gene conversion, gene reversion, gene drift, and nucleotide addition were incorporated into the algorithm in order to improve the balance between exploitation and exploration. In addition, niche antibody was employed to discover multi-modal solutions. Numerous well-studied benchmark examples in job-shop scheduling problems were utilized to evaluate the proposed approach. The results indicate the effectiveness and flexibility of the immune algorithm.