An Online Prediction Approach Based on Incremental Support Vector Machine for Dynamic Multiobjective Optimization
This work addresses dynamic multiobjective optimization for real-world applications like scheduling or engineering design, but it is incremental as it builds on existing prediction-based evolutionary methods.
The paper tackles dynamic multiobjective optimization problems where objectives change over time by proposing ISVM-DMOEA, a novel prediction algorithm based on incremental support vector machine, which uses historical optimal solutions to generate initial populations and achieves effective performance in experiments compared to state-of-the-art algorithms.
Real-world multiobjective optimization problems usually involve conflicting objectives that change over time, which requires the optimization algorithms to quickly track the Pareto optimal front (POF) when the environment changes. In recent years, evolutionary algorithms based on prediction models have been considered promising. However, most existing approaches only make predictions based on the linear correlation between a finite number of optimal solutions in two or three previous environments. These incomplete information extraction strategies may lead to low prediction accuracy in some instances. In this paper, a novel prediction algorithm based on incremental support vector machine (ISVM) is proposed, called ISVM-DMOEA. We treat the solving of dynamic multiobjective optimization problems (DMOPs) as an online learning process, using the continuously obtained optimal solution to update an incremental support vector machine without discarding the solution information at earlier time. ISVM is then used to filter random solutions and generate an initial population for the next moment. To overcome the obstacle of insufficient training samples, a synthetic minority oversampling strategy is implemented before the training of ISVM. The advantage of this approach is that the nonlinear correlation between solutions can be explored online by ISVM, and the information contained in all historical optimal solutions can be exploited to a greater extent. The experimental results and comparison with chosen state-of-the-art algorithms demonstrate that the proposed algorithm can effectively tackle dynamic multiobjective optimization problems.