Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6903154 | Swarm and Evolutionary Computation | 2018 | 17 Pages |
Abstract
The issue of integrating preference information into multi-objective optimization is considered, and a multi-objective framework based on decomposition and preference information, called indicator-based MOEA/D (IBMOEA/D), is presented in this study to handle the multi-objective optimization problems more effectively. The proposed algorithm uses a decomposition-based strategy for evolving its working population, where each individual represents a subproblem, and utilizes a binary quality indicator-based selection for maintaining the external population. Information obtained from the quality improvement of individuals is used to determine which subproblem should be invested at each generation by a power law distribution probability. Thus, the indicator-based selection and the decomposition strategy can complement each other. Through the experimental tests on seven many-objective optimization problems and one discrete combinatorial optimization problem, the proposed algorithm is revealed to perform better than several state-of-the-art multi-objective evolutionary algorithms. The effectiveness of the proposed algorithm is also analyzed in detail.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Jianping Luo, Yun Yang, Xia Li, Qiqi Liu, Minrong Chen, Kaizhou Gao,