An artificial immune network for multimodal function optimization on dynamic environments

Multimodal optimization algorithms inspired by the immune system are generally characterized by a dynamic control of the population size and by diversity maintenance along the search. One of the most popular proposals is denoted opt-aiNet (artificial immune network for optimization) and is extended here to deal with time-varying fitness functions. Additional procedures are designed to improve the overall performance and the robustness of the immune-inspired approach, giving rise to a version for dynamic optimization, denoted dopt-aiNet. Firstly, challenging benchmark problems in static multimodal optimization are considered to validate the new proposal. No parameter adjustment is necessary to adapt the algorithm according to the peculiarities of each problem. In the sequence, dynamic environments are considered, and usual evaluation indices are adopted to assess the performance of dopt-aiNet and compare with alternative solution procedures available in the literature.

[1]  Mokhtar S. Bazaraa,et al.  Nonlinear Programming: Theory and Algorithms , 1993 .

[2]  Fernando José Von Zuben,et al.  Learning and optimization using the clonal selection principle , 2002, IEEE Trans. Evol. Comput..

[3]  N K Jerne,et al.  Towards a network theory of the immune system. , 1973, Annales d'immunologie.

[4]  Patrick Siarry,et al.  Enhanced simulated annealing for globally minimizing functions of many-continuous variables , 1997, TOMS.

[5]  Xin Yao,et al.  Fast Evolution Strategies , 1997, Evolutionary Programming.

[6]  Alan S. Perelson,et al.  The immune system, adaptation, and machine learning , 1986 .

[7]  Jonathan Timmis,et al.  Assessing the performance of two immune inspired algorithms and a hybrid genetic algorithm for function optimisation , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[8]  A. Sidow,et al.  Gene duplications and the origins of vertebrate development. , 1994, Development (Cambridge, England). Supplement.

[9]  G L Ada,et al.  The clonal-selection theory. , 1987, Scientific American.

[10]  Dr. Susumu Ohno.  Evolution by Gene Duplication , 1970, Springer Berlin Heidelberg.

[11]  A. Pétrowski,et al.  A classification tree for speciation , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[12]  Peter J. Angeline,et al.  Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences , 1998, Evolutionary Programming.

[13]  Alan S. Perelson,et al.  Self-nonself discrimination in a computer , 1994, Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy.

[14]  P E Seiden,et al.  A model for simulating cognate recognition and response in the immune system. , 1992, Journal of theoretical biology.

[15]  Peter J. Angeline,et al.  Tracking Extrema in Dynamic Environments , 1997, Evolutionary Programming.

[16]  Yuping Wang,et al.  An orthogonal genetic algorithm with quantization for global numerical optimization , 2001, IEEE Trans. Evol. Comput..

[17]  Jonathan Timmis.  Artificial immune systems : a novel data analysis technique inspired by the immune network theory , 2000 .

[18]  Jonathan Timmis,et al.  Artificial immune systems - a new computational intelligence paradigm , 2002 .

[19]  H. Abbass,et al.  aiNet : An Artificial Immune Network for Data Analysis , 2022 .

[20]  Jonathan Timmis,et al.  Chasing chaos , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[21]  Fernando José Von Zuben,et al.  Immune and Neural Network Models: Theoretical and Empirical Comparisons , 2001, Int. J. Comput. Intell. Appl..

[22]  T. Kepler,et al.  Somatic hypermutation in B cells: an optimal control treatment. , 1993, Journal of theoretical biology.

[23]  Jonathan Timmis,et al.  Artificial Immune Systems: A New Computational Intelligence Approach , 2003 .

[24]  A. Perelson.  Immune Network Theory , 1989, Immunological reviews.

[25]  G. Oster,et al.  Theoretical studies of clonal selection: minimal antibody repertoire size and reliability of self-non-self discrimination. , 1979, Journal of theoretical biology.

[26]  Jonathan Timmis,et al.  A Comment on Opt-AiNET: An Immune Network Algorithm for Optimisation , 2004, GECCO.

[27]  L.N. de Castro,et al.  An artificial immune network for multimodal function optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

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

[29]  Joanne H. Walker,et al.  Dynamic Function Optimisation: Comparing the Performance of Clonal Selection and Evolution Strategies , 2003, ICARIS.

[30]  C. Kocks,et al.  Timing, Genetic Requirements and Functional Consequences of Somatic Hypermutation during B‐Cell Development , 1987, Immunological reviews.

[31]  Philippe Collard,et al.  From GAs to artificial immune systems: improving adaptation in time dependent optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[32]  F. Varela,et al.  Second generation immune networks. , 1991, Immunology today.

[33]  Rajarshi Das,et al.  A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization , 1989, ICGA.