Multi-objective Optimization Algorithm Based on Clonal Selection

In this paper, we propose a new multi-objective optimization approach based the clonal selection principle which is from an artificial immune system. Our approach uses the cluster method in the memory cell set of the clonal selection principle to renew and eliminate antibody; the non-uniform mutation operator is employed to the multiplicity of population. This algorithm cannot promote to individual competition and universality of populations by using antibody-antibody affinity but restrain excessive competition by using antigen-antigen affinity. In our study, a test function and some metrics commonly adopted in multi-objective optimization are used. Our results indicate that the proposed approach has its performances better than those produced by NSGA-square, and closer to Pareto-curve which is a set of the uniform and widespread solutions.