Abstract
A novel immune algorithm with dynamic environments (IADE), suitable for time-varying single-objective optimization problems with the static or variable dimension of design space, is proposed based on the immune response principle. Several immune operators, relying upon the functions or metaphors of somatic maturation, immune memory and immune cells, are designed to adapt the changing environment and track the location of the optimum. Especially, the environmental recognition rule and memory pool are established to speed up to search the optimum of the environment. Several algorithms reported are participated in comparison against IADE through using theoretical test problems and a practical greenhouse control problem. Preliminary experiments show that IADE can not only obtain great superiority, but also track rapidly time-varying environments. Comparative analysis illustrates IADE’s potential value.
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