An Adaptive Hybrid Immune Algorithm for Furniture Model Design Problem

摘要

For the furniture model design optimization problem, this paper proposes an Adaptive Hybrid Immune Genetic Algorithm. It generates initial antibody set by searching according to the fuzzy subjection of the semantic of target design in the initialization stage, and extracts vaccine set according to the advices of experts. Then vaccination and affinity-based selection are performed in the evolution process of antibodies to accelerate the convergence of antibodies and preserve population diversity. The fitness function is defined with a BP neural network which maps antibodies from the semantic space of feature element of model design to the semantic space of the emotion. In addition, the vaccination possibilities of vaccines adapt to the effects of vaccination and the affinities and selection possibilities of antibodies are defined according to the information entropy theory. The result of prototype system shows that our algorithm can produce satisfying model design scheme.