Decomposition-based sub-problem optimal solution updating direction-guided evolutionary many-objective algorithm

Abstract The many-objective optimization problem (MaOP) is a common problem in the fields of engineering and scientific computing. It requires the optimization of multiple conflicting objectives. Due to the complexity of the MaOP, its optimization requires considerable amounts of time and computation resources to execute. Moreover, demand for a general optimization method for different types of MaOPs is becoming increasingly urgent. In this paper, the reference-vector-guided evolutionary algorithm (RVEA) is modified to accelerate the optimization speed and to improve its adaptability. To achieve more rapid convergence, a sub-problem optimal solution updating direction-guided variation strategy is developed to replace the original variation strategy of the RVEA. A comparative experiment on the typical test suites verifies that the proposed method offers preferable performance. Our experiment shows that the performance of the OD-RVEA declines when optimizing MaOPs with irregular Pareto fronts (PFs). To address this issue, an adaptive reference vector adjustment strategy is designed as a means of enhancing the optimization capabilities of MaOPs with irregular PFs by adjusting the distribution of reference vectors. Our comparative experiment on test cases that involve irregular PFs shows that the algorithm that applies this strategy outperforms the algorithm that applies fixed reference vectors.