Institute of Electrical Engineering, Yanshan University
National Natural Science Foundation of China(62003296)；Natural Science Foundation of Hebei(F2020203031)；Science and Technology Project of Hebei Education Department(QN2020225)
When dealing with many-objective optimization problems, the evolutionary algorithm based on decomposition is proved to be a effective method. The traditional decomposition relys on a set of uniformly distributed reference vector. This method decomposes the multi-objective optimization problem into a set of single-objective subproblems through aggregation functions, and then optimizes these subproblems simultaneously. However, these predefined reference vectors perform poorly in solving complex many-objective optimization problems because of the inconsistency of the distribution of reference vectors and the shape of the Pareto front. Aiming at the above problems, a many-objective evolutionary algorithm based on adaptive boosting learning (MaOEA-ABL) is proposed. The algorithm can be divided into two stages. In the first stage, an adaptive boosting learning algorithm is used to adjust the predefined reference vectors. In the learning process, useless vectors are deleted and new vectors are added. In the second stage, an unbiased decomposition method of Pareto shape is designed. Simulation has been conducted on MaF test problems. The experimental results show that the IGD mean value of MaOEA-ABL is better than that of the comparison algorithms in 67% of the test functions, which indicates that MaOEA-ABL performs well in many-objective optimization problems with complex Pareto front.