Knowledge Transfer for Dynamic Multi-Objective Optimization With a Changing Number of Objectives

作     者：Ruan, Gan Minku, Leandro L. Menzel, Stefan Sendhoff, Bernhard Yao, Xin 

作者机构：Univ Birmingham CERCIA Sch Comp Sci Birmingham B15 2SQ W Midlands England Honda Res Inst Europe GmbH D-63073 Offenbach Germany Lingnan Univ Dept Comp & Decis Sci Hong Kong Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE》 (IEEE Trans. Emerging Topics Comp. Intell.)

年 卷 期：2024年第8卷第6期

页      面：4210-4224页

核心收录：

基　　金：National Natural Science Foundation of China Guangdong Provincial Key Laboratory [2020B121201001] Program for Guangdong Introducing Innovative and Entrepreneurial Teams [2017ZT07X386] European Union 

主　　题：Dynamic optimization changing objectives knowledge transfer evolutionary algorithms multi-objective optimization 

摘      要：Different from most other dynamic multi-objective optimization problems (DMOPs), DMOPs with a changing number of objectives usually result in expansion or contraction of the Pareto front or Pareto set manifold. Knowledge transfer has been used for solving DMOPs, since it can transfer useful information from solving one problem instance to solve another related problem instance. However, we show that the state-of-the-art transfer algorithm for DMOPs with a changing number of objectives lacks sufficient diversity when the fitness landscape and Pareto front shape present nonseparability, deceptiveness or other challenging features. Therefore, we propose a knowledge transfer dynamic multi-objective evolutionary algorithm (KTDMOEA) to enhance population diversity after changes by expanding/contracting the Pareto set in response to an increase/decrease in the number of objectives. This enables a solution set with good convergence and diversity to be obtained after optimization. Comprehensive studies using 13 DMOP benchmarks with a changing number of objectives demonstrate that our proposed KTDMOEA is successful in enhancing population diversity compared to state-of-the-art algorithms, improving optimization especially in fast changing environments.