Adaptive boosting learning evolutionary algorithm for complex many-objective optimization problems

被引：0
|
作者：
Hu Z.-Y. [1 ,2 ]
Li Y.-L. [1 ,2 ]
Wei Z.-H. [1 ,2 ]
Yang J.-M. [1 ,2 ]
机构：
[1] School of Electrical Engineering, Yanshan University, Qinhuangdao
[2] Engineering Research Center, The Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao
来源：
Kongzhi yu Juece/Control and Decision | 2022年 / 37卷 / 11期
关键词：
adaptive boosting learning; adjustment of reference vector; complex Pareto front; decomposition; evolutionary algorithm; many-objective optimization;
D O I：
10.13195/j.kzyjc.2021.0725
中图分类号：
学科分类号：
摘要：
The evolutionary algorithm based on decomposition is an effective method in dealing with many-objective optimization problems. The traditional decomposition method relys on a set of uniformly distributed reference vectors, which 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 the MaF test problems. The experimental results show that the IGD (inverted generational distance) mean value of MaOEA-ABL is better than that of the comparison algorithms in 67 % of the test functions, which indicates that the MaOEA-ABL performs well in many-objective optimization problems with complex Pareto front. © 2022 Northeast University. All rights reserved.
引用
收藏
页码：2849 / 2859
页数：10
相关论文
共 25 条
  • [11] Liu J C, Li F, Wang H H, Et al., Survey on evolutionary many-objective optimization algorithms, Control and Decision, 33, 5, pp. 879-887, (2018)
  • [12] Cai X Y, Mei Z W, Fan Z., A decomposition-based many-objective evolutionary algorithm with two types of adjustments for direction vectors, IEEE Transactions on Cybernetics, 48, 8, pp. 2335-2348, (2018)
  • [13] Jiang S Y, Yang S X., An improved multiobjective optimization evolutionary algorithm based on decomposition for complex Pareto fronts, IEEE Transactions on Cybernetics, 46, 2, pp. 421-437, (2016)
  • [14] Asafuddoula M, Singh H K, Ray T., An enhanced decomposition-based evolutionary algorithm with adaptive reference vectors, IEEE Transactions on Cybernetics, 48, 8, pp. 2321-2334, (2018)
  • [15] Jiang S Y, He X Y, Zhou Y R., Many-objective evolutionary algorithm based on adaptive weighted decomposition, Applied Soft Computing, 84, (2019)
  • [16] Elarbi M, Bechikh S, Coello Coello C A, Et al., Approximating complex Pareto fronts with predefined normal-boundary intersection directions, IEEE Transactions on Evolutionary Computation, 24, 5, pp. 809-823, (2020)
  • [17] Zhang Q S, Zhu W, Liao B, Et al., A modified PBI approach for multi-objective optimization with complex Pareto fronts, Swarm and Evolutionary Computation, 40, pp. 216-237, (2018)
  • [18] Ge H W, Zhao M D, Sun L, Et al., A many-objective evolutionary algorithm with two interacting processes: Cascade clustering and reference point incremental learning, IEEE Transactions on Evolutionary Computation, 23, 4, pp. 572-586, (2019)
  • [19] Li X Q, Liu H L., New evolutionary algorithm for complicated multiobjective optimization, Journal of System Simulation, 23, 9, pp. 1860-1865, (2011)
  • [20] Freund Y, Schapire R E., A decision-theoretic generalization of on-line learning and an application to boosting, Journal of Computer and System Sciences, 55, 1, pp. 119-139, (1997)
123