Dynamic immune algorithm based on simulated annealing selection and its application
A novel dynamic immune optimization algorithm(DIASA),based on simulated annealing selection theory,adaptive memory and the functions of dynamic recognition of immune system,is proposed to solve the high-dimensional dynamic knapsack problem with constraints.The keys of algorithm are included:(1)The affinity of antibody is designed based on the performance of current population(.2)The infeasible antibodies are repaired by the increasing sorting of price consistency of antibodies gene,while the feasible antibodies are cloned and mutated dynamically,the mutation probability is designed by the density of antibody(.3)The new environmental population is generated using memory cells according to environmental recognition operator,which accelerates the convergence of algorithm.In numerical experiments,two existing intelligent algorithms ETGA and ISGA for dynamic optimization problem are selected to compare with the algorithm designed for dynamic highdimension knapsack problem,the results indicate that the DIASA shows a promising convergent capability,and can track rapidly the optimum,powerful diversity of population.