An Improvement Study of the Decomposition-based Algorithm Global WASF-GA for Evolutionary Multiobjective Optimization Sandra Gonzalez-Gallardo, Rub´ en Saborido, Ana B. Ruiz, and Mariano Luque Department of Applied Economics (Mathematics), University of M´ alaga, C/ Ejido 6, 29071, M´ alaga, Spain sandragg@uma.es,rsain@uma.es,abruiz@uma.es,mluque@uma.es Abstract. The convergence and the diversity of the decomposition- based evolutionary algorithm Global WASF-GA (GWASF-GA) relies on a set of weight vectors that determine the search directions in the ob- jective space for new non-dominated solutions. Although using weight vectors whose search directions are widely distributed may lead to a well-diversified approximation of the Pareto front (PF), this fact may not perform as expected for complicated PFs (discontinuous, not con- vex, etc.). To handle this, we propose an adjustment of the weight vec- tors once GWASF-GA has been run for certain number of generations. This dynamic adjustment is aimed at re-calculating some of the weight vectors, so that search directions pointing to overcrowded regions of the PF are redirected toward parts with a lack of solutions that may be hard to be approximated. We test different parameters settings of this dy- namic adjustment in optimization problems with three, five, and six ob- jectives. We conclude that GWASF-GA performs better when adjusting the weight vectors dynamically than without applying the adjustment. Keywords: Evolutionary multiobjective optimization · Decomposition- based algorithm · Global WASF-GA · Weight vector.