This paper proposes a new prediction-based dynamic multiobjective optimization (PBDMO) method, which combines a new prediction-based reaction mechanism and a popular regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA) for solving dynamic multiobjective optimization problems. Whenever a change is detected, PBDMO reacts effectively to it by generating three subpopulations based on different strategies. The first subpopulation is created by moving nondominated individuals using a simple linear prediction model with different step sizes. The second subpopulation consists of some individuals generated by a novel sampling strategy to improve population convergence as well as distribution. The third subpopulation comprises some individuals generated using a shrinking strategy based on the probability distribution of variables. These subpopulations are tailored to form a population for the new environment. The 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.
Bibliographical noteFunding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61673331, Grant 61873006, Grant 61473034, and Grant 61673053, in part by the Shenzhen Peacock Plan under Grant KQTD2016112514355531, in part by the Beijing Science and Technology Major Project under Grant Z18110003118012, and in part by the National Key Research and Development Project under Grant 2018YFC1602704 and Grant 2018YFB1702704.
- Dynamic multiobjective optimization
- Nondominated sorting
- Prediction-based reaction
- Probability distribution