Weighted Optimization Framework for Large-scale Multi-objective Optimization

Abstract

In this work we introduce a new method for solving multi-objective optimization problems that involve a large number of decision variables. The proposed Weighted Optimization Framework (WOF) relies on variable grouping and weighting to transform the original optimization problem and is designed as a generic method that can be used with any population-based algorithm. Our experiments use the WFG benchmark problems with 2 and 3 objectives and 1000 variables. Using WOF on two well-known algorithms (NSGA-II and SMPSO), we show that our method can significantly improve their performance on all of the test problems.