An objective reduction method based on advanced clustering for many-objective optimization problems and its human-computer interaction visualization of pareto front

Abstract

Many-objective optimization problems(MaOPs) are the most challenging problems among multi-objective optimization problems (MOPs). Objective reduction method has become one of the most important technique for MaOPs which can alleviate the difficulties of selection pressure, computational cost and the human-computer interaction visualization. In this paper, we propose an objective reduction algorithm based on adaptive propagating tree clustering for MaOPs. The advanced adaptive clustering method makes the number of clusters determined adaptively, and outliers can be clustered correctly. According to the clustering result, the algorithm uses an adaptive objective aggrega tion method which can preserve the structure of the original problem as much as possible. The algorithm is suitable for dealing with MaOPs with irregular shape of sample sets, and can improve the friendliness of human-computer interaction visualization of Pareto Front. Compared with different types of classical many-objective optimization algorithms, the simulation results of our algorithm has considerable advantages.