Abstract: This paper presents an Orthogonal Design-based Dynamic Multi-Objective Optimization Algorithm（ODMOA）. The algorithm makes use of the historical optimal solution, and then predicts a new population by considering the properties of DOPs when an environmental change is detected. Otherwise, it will cause orthogonal experimental method to make a systematic and rational search in the solution space, which makes it converge to the optima faster. Some comparison experiments are carried out and the results prove the effectiveness of the algorithm.

Key words: dynamic multi-objective optimization, Pareto optimal solution set, orthogonal design, environment detection

摘要： 提出了一种基于正交设计的动态多目标优化算法（ODMOA），当环境变化时通过分析动态多目标优化问题的特点，利用历史信息对新环境下的Pareto最优解集进行预测，得到一个新的预测种群；否则在静态环境下使用正交试验法在解空间内进行系统且高效的搜索，使算法能够在当前环境下快速收敛到最优解。进行了多组对比试验，验证了该算法的有效性。

关键词: 动态多目标优化, Pareto最优解集, 正交设计, 环境检测