Immune optimization algorithm for constrained nonlinear multiobjective optimization problems

A new dynamical immune optimization algorithm for constrained nonlinear multiobjective optimization problems over continuous domains is proposed based on both the concept of Pareto optimality and simple interactive metaphors between antibody population and multiple antigens as well as ideas of T cell regulation. The focus of design is concentrated on constructing one constraint-handling technique associated with uniform design reported and designing one antibody evolution mechanism through utilizing simplified metaphors of humoral immune response of the immune system. The former is to provide an alternative feasible solution set for dealing with constraints and infeasible solutions created during the execution of the algorithm, while helping for rapidly finding Pareto-optimal solutions; the latter generates multiple excellent feasible solutions so that the desired solutions will be gradually obtained. Theoretically, its weak convergence is proven by using Markov theory, while the experimental results demonstrate its strong convergence. Through application to difficult test problems, comparative results illustrate it is potential for the algorithm to cope with high dimensional complex optimization problems with multiple constraints.