A Decomposition-Based Multiobjective Optimization Evolutionary Algorithm with Adaptive Weight Generation Strategy. (2nd September 2021)

• Record Type: Journal Article
• Authors: Fu, Guo-Zhong; Yu, Tianda; Li, Wei; Deng, Qiang; Yang, Bo
• Other Names: Yazdi Mohammad Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2021(2021)
• Publication Date: 2021-09-02
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2021/2764558
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Multiobjective evolutionary algorithm based on decomposition (MOEA/D) is the seminal framework of multiobjective evolutionary algorithms (MOEAs). To alleviate the nonuniformly distributed solutions generated by a fixed set of evenly distributed weight vectors in the presence of nonconvex and disconnected problems, an adaptive vector generation mechanism is proposed. A coevolution strategy and a vector generator are synergistically cooperated to remedy the weight vectors. Optimal weight vectors are generated to replace the useless weight vectors to assure that optimal solutions are distributed evenly. Experiment results indicate that this mechanism is efficient in improving the diversity of MOEA/D.