Optimizing constrained problems through a T-Cell artificial immune system

• Autores: Victoria S. Aragón, Susana Cecilia Esquivel, Carlos Coello Coello
• Localización: Journal of Computer Science and Technology, ISSN-e 1666-6038, Vol. 8, Nº. 3, 2008 (Ejemplar dedicado a: Twenty-Fourth Issue), págs. 158-165
• Idioma: inglés
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• Resumen

  • In this paper, we present a new model of an artificial immune system (AIS), based on the process that suffers the T-Cell, it is called T-Cell Model. It is used for solving constrained (numerical) optimization problems. The model operates on three populations: Virgins, Effectors and Memory. Each of them has a different role. Also, the model dynamically adapts the tolerance factor in order to improve the exploration capabilities of the algorithm. We also develop a new mutation operator which incorporates knowledge of the problem. We validate our proposed approach with a set of test functions taken from the specialized literature and we compare our results with respect to Stochastic Ranking (which is an approach representative of the state-of-theart in the area), with respect to an AIS previously proposed and a self-organizing migrating genetic algorithm for constrained optimization (C-SOMGA).