A clonal selection adaptive local search operator for multi-objective optimization evolutionary algorithm

A clonal selection adaptive local search operator for multi-objective optimization evolutionary algorithm is proposed in order to enhance the search capability and expedite convergence speed of the multi-objective evolutionary algorithms. A crossover based adaptive local search algorithm including a method to change the clonal scale of different individuals adaptively according to their position in the whole population is proposed. Test functions with two objectives and three objectives are selected to confirm the performance of the operator. Results show that the clonal selection adaptive local operator makes multi-objective optimization evolutionary algorithm has better performance in convergence and distribution.