A framework for high-dimensional robust evolutionary multi-objective optimization

ABSTRACT

This paper proposes a framework for solving high-dimensional robust multi-objective optimization problems. A decision variable classification-based framework is developed to search for robust Pareto-optimal solutions. The decision variables are classified as highly and weakly robustness-related variables based on their contributions to the robustness of candidate solutions. In the case study, an order scheduling problem in the apparel industry is investigated via the proposed framework. The experimental results reveal that the performance of robust evolutionary optimization can be greatly improved via analyzing the properties of decision variables and then decomposing the high-dimensional robust multi-objective optimization problem.

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