论文信息 - An Adaptive Immune Genetic Algorithm and Its Application

An Adaptive Immune Genetic Algorithm and Its Application

To avoid premature and guarantee the diversity of the population, an adaptive immune genetic algorithm(AIGA) is proposed to solve these problems. In this method, the AIGA flow structure is presented via combining the immune regulating mechanism and the genetic algorithm. Experimental results showed that the proposed AIGA can rise above efficiently such difficulties of SGA as precocious convergence and poor local search ability and provide well the global converging ability to enhance both global convergency and convergence rate, thus solving effectively the flexible job-shop scheduling problem (FJSP).

[1] Lalit M. Patnaik,et al. Adaptive probabilities of crossover and mutation in genetic algorithms , 1994, IEEE Trans. Syst. Man Cybern..

[2] Varghese S. Jacob,et al. A genetics-based hybrid scheduler for generating static schedules in flexible manufacturing contexts , 1993, IEEE Trans. Syst. Man Cybern..

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

[4] Licheng Jiao,et al. A novel genetic algorithm based on immunity , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[5] John E. Hunt,et al. Learning using an artificial immune system , 1996 .

[6] D. Dasgupta. Artificial Immune Systems and Their Applications , 1998, Springer Berlin Heidelberg.

[7] Shyh-Jier Huang. Enhancement of thermal unit commitment using immune algorithms based optimization approaches , 1999 .

[8] N.M. Najid,et al. A modified simulated annealing method for flexible job shop scheduling problem , 2002, IEEE International Conference on Systems, Man and Cybernetics.

[9] Nhu Binh Ho,et al. GENACE: an efficient cultural algorithm for solving the flexible job-shop problem , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).