A new multiobjective evolutionary optimization algorithm based on θ-multiobjective clonal selection

In this paper a new multi-objective clonal selection algorithm (theta-MCSA) is presented to solve multi-objective problems with multimodal and non-continuous functions. The concept of clonal selection algorithm (CSA) is based on the immune system and white blood cells behavior that select the antibodies similar to antigen for cloning. Although the clonal selection is a robust optimization method, however, as a shortcoming, it takes long time to find optimal Pareto front especially in problems with large search space. To overcome this problem, the proposed method replaces the large search space with the theta-search based on the phase angles. To avoid trapping into local optima in mutation step, two strong mutation methods are implemented according to the iteration number and algorithm efficiency. For converging to uniformly Pareto front in less iterations, the proposed multi-objective algorithm handles the size of the repository and a new population updating mechanism is iteratively applied to select the non-dominate, one-dominate and two-dominate solutions of prior iteration. The experimental results show the efficiency of the proposed theta-MCSA algorithm compared to other methods.