An Unconstrained Auxiliary Framework for Constrained Many-Objective Optimization

水准点(测量) 计算机科学 数学优化 任务(项目管理) 约束(计算机辅助设计) 约束优化 集合(抽象数据类型) 最优化问题 多目标优化 进化算法 人工智能 数学 算法 机器学习 工程类 几何学 程序设计语言 系统工程 地理 大地测量学
作者
Kangjia Qiao,Jing Liang,Ying Bi,Kunjie Yu,Caitong Yue,Boyang Qu
标识
DOI:10.1109/docs60977.2023.10295009
摘要

Constrained many-objective optimization problems (CMaOPs) include the optimization of many objective functions and satisfaction of constraints, which seriously enhance the difficulty of problems. Although several constrained many-objective evolutionary algorithms (CMaOEAs) have been designed, they still have difficulties in tackling many objectives and constraints at the same time. To better solve CMaOPs, this paper proposes an unconstrained auxiliary framework, in which an auxiliary task without constraints is developed to reduce the search difficulties of constraints. Moreover, to tackle many objectives, the existing CMaOEAs are employed to address the auxiliary task, in which the constraint values of solutions are set to zeros. In the experiments, one classic CMaOEA and two latest CMaOEAs are integrated into the framework to form three new algorithms. The results show the effectiveness and superiority of the framework. Besides, the winner among three new algorithms is compared with several existing CMaOEAs and shows better results. Meanwhile, we discuss the reasons that why the unconstrained framework is effect for the existing benchmark functions. Accordingly, we refer to that new test functions are urgently needed for the development of constrained many-objective optimization.
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