Data Stream Driven Dynamic Multiobjective Optimization Using Surrogate Transfer

Most existing dynamic multiobjective evolutionary algorithms assume that the real objective function evaluations are easily acquired during the optimization process. However, in some situations, the optimization algorithm cannot invoke the objective functions and is exclusively driven by the solutions within the continuously incoming data stream. Although these solutions come with their own objective values, the objective function associated with the solutions in the data stream experiences continuous changes across time. These problems are called data stream driven dynamic multiobjective optimization problems (DDMOPs). The difficulties of the DDMOPs are tracking the optimal solutions based on the dynamic data streams and extracting the historical information to improve the optimization performance. In this work, a multiobjective evolutionary algorithm framework (ST-MOEA) is proposed to address the above challenges. ST-MOEA uses the Kriging and RBFN models to separately substitute the objective functions and guide the optimization process. Two effective mechanisms named the surrogate transfer and reliable solution selection strategies are also proposed to further improve the algorithm performance. The surrogate transfer strategy transfers the surrogate hyperparameters used in the historical data streams to the current surrogate models to enhance the surrogate accuracies, and the reliable solution selection strategy identifies dependable solutions from the populations generated by two surrogates in order to ultimately construct the optimal solution set. During the experiment process, the proposed framework is embedded into three different multiobjective evolutionary algorithms and compared with two surrogate-assisted evolutionary algorithms on a wide range of test problems with varying decision dimensions and different levels of severity in objective function changes. The experiment results validate the effectiveness of the two proposed strategies and demonstrate the overall advantage of ST-MOEA to the compared algorithms.