An Adaptive Multi-objective Immune Optimization Algorithm

It is difficult for traditional search methods to solve multi-objective optimization problems. Based on the idea of clonal selection principle, we present an adaptive multi-objective immune optimization algorithm (AMIOA) for function optimization problems and analyze its powerful performance from the immune system point of view. The main feature of the algorithm is the global search performance and the solution sets produced are highly competitive in terms of convergence, diversity and distribution. The comparative simulation results show that the proposed algorithm not only can obtain a set of solutions including the global optimum and multiple local optima, but also has much less computational cost than other algorithms.