TY - JOUR AU - Chen, Li AU - Xu, Hua PY - 2020/04/03 Y2 - 2024/03/29 TI - CORAL-DMOEA: Correlation Alignment-Based Information Transfer for Dynamic Multi-Objective Optimization (Student Abstract) JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 10 SE - Student Abstract Track DO - 10.1609/aaai.v34i10.7154 UR - https://ojs.aaai.org/index.php/AAAI/article/view/7154 SP - 13765-13766 AB - <p>One essential characteristic of dynamic multi-objective optimization problems is that Pareto-Optimal Front/Set (POF/POS) varies over time. Tracking the time-dependent POF/POS is a challenging problem. Since continuous environments are usually highly correlated, past information is critical for the next optimization process. In this paper, we integrate CORAL methodology into a dynamic multi-objective evolutionary algorithm, named CORAL-DMOEA. This approach employs CORAL to construct a transfer model which transfer past well-performed solutions to form an initial population for the next optimization process. Experimental results demonstrate that CORAL-DMOEA can effectively improve the quality of solutions and accelerate the evolution process.</p> ER -