Contrastive-Learning-Based Decision Making for Dynamic Time-Linkage Optimization
针对动态时间关联优化中决策数据不足导致预测模型精度低的问题，提出基于对比学习的决策方法，通过构建对比模型学习解之间的相对关系，并集成到粒子群优化中，在多个测试实例和移动机器人路径规划问题上优于现有算法。
In dynamic time-linkage optimization, current decisions influence the future state of environments. To make good decisions that have a positive impact on future states, existing methods usually build a model to predict the future rewards of solutions for decision making. However, these prediction models present low accuracy since decision data are not enough to train such a complex model. To address this issue, this article proposes a contrastive-learning-based decision making (CLDM) method, which builds a contrastive model to learn the relationship between solutions but not absolute rewards and adopts a quick decision strategy to select solutions. In CLDM, a clustering-based time-linkage detection (CD) strategy is developed to measure the intensity of the time linkage, which determines whether to make decisions based on future rewards. To represent the relative relationship between solutions, a large number of contrastive samples are constructed using the limited historical decisions. A contrastive model is trained for solution comparison in terms of the combination of current fitness and future rewards. Candidate solutions are clustered into multiple groups to filter poor ones, and a few solutions are preserved to rank using the contrastive model. The winner is taken as the decision solution. Integrating CLDM into particle swarm optimization (PSO), a new algorithm named contrastive-learning-based PSO (CL-PSO) is put forward. Experimental results on multiple dynamic time-linkage optimization instances demonstrate that CL-PSO outperforms state-of-the-art algorithms in terms of solution quality. CL-PSO can also well solve the mobile robot path planning problem.