首页 | 本学科首页   官方微博 | 高级检索  
     


Many-objective evolutionary algorithm based on adaptive weighted decomposition
Affiliation:1. State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;2. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, Guangdong 510640, China;3. Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hung Hom, Hong Kong, China;4. Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA;5. Faculty of Engineering, University of Strathclyde, Glasgow G1 1XQ, UK;1. Department of Computer Science and Technology, Ocean University of China, 266100 Qingdao, China;2. Department of Computer Science, State University of New York, New Paltz, New York, 12561, USA;3. Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan;4. School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China;1. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China;2. The Department of Computer Science, University of Surrey, Guildford GU2 7XH, U.K.;3. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200237, China
Abstract:Decomposition is a representative method for handling many-objective optimization problems with evolutionary algorithms. Classical decomposition scheme relies on a set of uniformly distributed reference vectors to divide the objective space into multiple subregions. This scheme often works poorly when the problem has an irregular Pareto front due to the inconsistency between the distribution of reference vectors and the shape of Pareto fronts. We propose in this paper an adaptive weighted decomposition based many-objective evolutionary algorithm to tackle complicated many-objective problems whose Pareto fronts may or may not be regular. Unlike traditional decomposition based algorithms that use a pre-defined set of reference vectors, the reference vectors in the proposed algorithm are produced from the population during the search. The experiments show that the performance of the proposed algorithm is competitive with other state-of-the-art algorithms and is less-sensitive to the irregularity of the Pareto fronts.
Keywords:Many-objective optimization  Evolutionary algorithm  Objective space decomposition  Adaptive weight generation
本文献已被 ScienceDirect 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号