A Clustering-Based Support Vector Classifier for Dynamic Time-Linkage Optimization

Dynamic time-linkage optimization problems (DTPs) bring challenges to existing evolutionary algorithms due to the influence of a current decision in the future. Existing methods usually model the rewards of a current decision in the future for prediction. However, these methods often present low prediction accuracy due to the lack of sufficient training data. In addition, they often require a long computational time. To address these issues, the problem of predicting rewards is converted into a simpler binary classification problem, which evaluates whether a current solution can bring positive or negative influence in the future. This paper proposes a clustering-based support vector classifier for solution evaluation. In the proposed method, the density of the time-linkage property is detected first. Historical data are divided using k-means clustering so as to train a support vector classifier for solution evaluation. Good solutions are selected to generate a final decision solution using a crossover operator. Integrating the clustering-based support vector classifier into particle swarm optimization, a new method named CSVC-PSO is put forward. Multiple instances are constructed using a recent DTP test suite with different types of time-linkage patterns and density. Experimental results demonstrate that the proposed CSVC-PSO outperforms state-of-the-art algorithms on most instances using a shorter time.