Endocrine-Immune Network and Its Application for Optimization

A novel artificial immune network model (EINET) based on the regulation of endocrine system is proposed. In this EINET for optimization, several operators are employed or revised which aim at faster convergence speed and better optimal solution. Further speaking, a new operator, hormonal regulation, exerts a bidirectional regulatory mechanism inspired from endocrine system, which undergoes elimination and mutation according to hormone updating function, to increase the diversity of antibody population. And antibody learning is an evolution of individuals through learning from memory antibody in immune network. Then, a local search procedure called enzymatic reaction is utilized to facilitate the exploitation of the search space and speed up the convergence. To evaluate whether the proposed model can be directly extended to an effective algorithm for solving combinatorial optimization problem, EINET-TSP algorithm is designed. Comparative experiments are conducted using some benchmark instances from the TSPLIB, and the results compared with the existing immune network applied to combinatorial optimization problem shows that the EINET-TSP algorithm is capable of improving search performance significantly in solution quality.