Novel prediction strategies for dynamic multi-objective optimization
This paper proposes a new prediction-based dynamic multi-objective optimization (PBDMO) method, which combines a new prediction-based reaction mechanism and a popular regularity model-based multi-objective estimation of distribution algorithm (RM-MEDA) for solving dynamic multi-objective optimization problems. Whenever a change is detected, PBDMO reacts effectively to it by generating three sub-populations based on different strategies. The first sub-population is created by moving non-dominated individuals using a simple linear prediction model with different step sizes. The second sub-population consists of some individuals generated by a novel sampling strategy to improve population convergence as well as distribution. The third sub-population comprises some individuals generated using a shrinking strategy based on the probability distribution of variables. These sub-populations are tailored to form a population for the new environment. Experimental results carried out on a variety of bi- and three-objective benchmark functions demonstrate that the proposed technique has competitive performance compared with some state-of-the-art algorithms.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
Citation : Q. Zhang, S. Yang, S. Jiang, R. Wang, and X. Li. (2019) Novel prediction strategies for dynamic multi-objective optimization. IEEE Transactions on Evolutionary Computation,
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes