论文信息 - An improved artificial immune algorithm with a dynamic threshold

An improved artificial immune algorithm with a dynamic threshold

An improved artificial immune algorithm with a dynamic threshold is presented. The calculation for the affinity function in the real-valued coding artificial immune algorithm is modified through considering the antibody’s fitness and setting the dynamic threshold value. Numerical experiments show that compared with the genetic algorithm and the originally real-valued coding artificial immune algorithm, the improved algorithm possesses high speed of convergence and good performance for preventing premature convergence.

[1] Jim Engle-Warnick,et al. The evolution of strategies in a repeated trust game , 2004 .

[2] Osama A. Mohammed,et al. Utilizing genetic algorithms for the optimal design of electromagnetic devices , 1994 .

[3] Guan-Chun Luh,et al. Multi-objective optimal design of truss structure with immune algorithm , 2004 .

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

[5] Nurhan Karaboga,et al. Artificial immune algorithm for IIR filter design , 2005, Eng. Appl. Artif. Intell..

[6] Song-Yop Hahn,et al. A study on comparison of optimization performances between immune algorithm and other heuristic algorithms , 1998 .

[7] J. Chun,et al. Shape optimization of electromagnetic devices using immune algorithm , 1997 .

[8] Aiguo Song,et al. An immune evolutionary algorithm for sphericity error evaluation , 2004 .

[9] Olivier François,et al. Global convergence for evolution strategies in spherical problems: some simple proofs and difficulties , 2003, Theor. Comput. Sci..

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

[11] Sy-Ming Guu,et al. Properties of the multiple lot-sizing problem with rigid demands and general yield distributions , 1997 .