Objective Reduction Using Conflict Information in Many-objective Optimization Evolutionary Algorithm

It is well-known that multi-objective evolutionary algorithms may suffer from convergence, computational complexity, decision-making and visualization of the Pareto fronts while solving many-objective optimization problems with high dimensionality of the objective space. Objective reduction provides an alternative for many-objective optimization by discarding the redundant objectives. This paper proposes objective reduction using conflict information in many-objective evolutionary algorithm which is based on decomposition, short for CIOR-MOEA/D. It measures the conflict information among objectives in the approximation solution set. By constructing conflict information matrix of the original problem and identifying the importance of objectives via matrix eigen-decomposition, dimension reduction of the original problem is achieved. Lastly, MOEA/D is used to optimize the important objectives, and the approximate solution set is obtained. Experimental results show that the new conflict information based objective reduction is outstanding in accuracy and robustness, and it can effectively solve the reducible MaOPs.