Multi-objective Immune Optimization in Dynamic Environments and Its Application to Signal Simulation

A novel immune optimization technique, associated to Pareto optimality and the humoral immunity of the immune system is proposed to solve a class of multi objective optimization problems with the time dependent decision space. Four immune operators, elitism evolution, rearrangement, immune regulation and memory pool, are designed to adapt to the changing environment so that the technique can achieve a reasonable tradeoff between convergence and diversity . Experimental results show that the proposed algorithm performs well over the algorithms compared.