A decomposition-based evolutionary algorithm with adaptive weight adjustment for many-objective problems

摘要

For many-objective optimization problems (MaOPs), how to get a set of solutions with good convergence and diversity is a difficult and challenging work. In this paper, a new decomposition-based evolutionary algorithm with adaptive weight adjustment is designed to obtain this goal. The proposed algorithm adopts the uniform design method to set the weight vectors which are uniformly distributed over the design space, and an adaptive weight adjustment is used to solve some MaOPs with complex Pareto optimal front (PF) (i.e., PF with a sharp peak of low tail or discontinuous PF). A selection strategy is used to help solutions to converge to the Pareto optimal solutions. Comparing with some efficient state-of-the-art algorithms, e.g., NSGAII-CE, MOEA/D and HypE, on some benchmark functions, the proposed algorithm is able to find more accurate Pareto front with better diversity.