Wang Ziqiang,Feng Boqin
(School of Electronics and Information Engineering,Xi'an Jiaotong University,Xi'an 710049,China)
Abstract:To efficiently deal with flow-shop scheduling problems,a novel algorithm,artifical immune algorithm is proposed which is inspired by the immune system of human and other mammals to simulate the process of the interaction between antigens,antibodies and lymphocytes.The implement of the artifical immune algorithm on flow-shop problems is as follows.The objective function and the part of inequality constraints serve as antigens and solutions serve as antibodies;the antibodies are encoded as natural number that is consistent with workpiece processing sequence;the fitness function is designed as the inversion of maximal flow time.New antibodies are produced by adopting the partially matched crossover operator and mutation operator permuted by work-pieces sequence.The promotion and suppression of antibodies are adjusted according to antibody concentration that is obtained from the maximal affinity value among antibodies.The proposed algorithm is tested on scheduling problem benchmarks.Experimental results show that immune algorithm is quite flexible with satisfactory results,and requires fewer ruuning time than genetic and simulated anneal algorithms．
Keywords:flow-shop scheduling;immune algorithm;antigen;antibody