A Uniform Evolutionary Algorithm Based on Decomposition and Contraction for Many-Objective Optimization Problems

For many-objective optimization problems, how to get a set of solutions with good convergence and diversity is a difficult and challenging task. To achieve this goal, a new evolutionary algorithm based on decomposition and contraction is proposed. Moreover, a sub-population strategy is used to enhance the local search ability and improve the convergence. The proposed algorithm adopts a contraction scheme of the non-dominance area to determine the best solution of each sub-population. The comparison with the several existing well-known algorithms: NSGAII, MOEA/D and HypE, on two kinds of benchmark functions with 5 to 25 objectives is made, and the results indicate that the proposed algorithm is able to obtain more accurate Pareto front with better diversity.