Due to the dynamics and uncertainty of the dynamic multi-objective optimization problems (DMOPs), it is difficult for algorithms to find a satisfactory solution set before the next environmental change, especially for some complex environments. One reason may be that the information in the environmental static stage can not be used well in the traditional framework. In this paper, a novel framework based on generational and environmental response strategies (FGERS) is proposed, in which response strategies are run both in the environmental change stage and the environmental static stage to obtain population evolution information of those both stages. Unlike in the traditional framework, response strategies are only run in the environmental change stage. For simplicity, the feed-forward center point strategy was chosen to be the response strategy in the novel dynamic framework (FGERS-CPS). FGERS-CPS is not only to predict change trend of the optimum solution set in the environmental change stage, but to predict the evolution trend of the population after several generations in the environmental static stage. Together with the feed-forward center point strategy, a simple memory strategy and adaptive diversity maintenance strategy were used to form the complete FGERS-CPS. On 13 DMOPs with various characteristics, FGERS-CPS was compared with four classical response strategies in the traditional framework. Experimental results show that FGERS-CPS is effective for DMOPs.
翻译:由于动态多目标优化问题的动态和不确定性,算法很难在下一个环境变化之前找到令人满意的解决办法,特别是对于一些复杂的环境,原因之一可能是传统框架不能很好地利用环境静态阶段的信息;在本文件中,提出了一个基于代际和环境反应战略的新框架,在环境变化阶段和环境静态阶段运行反应战略,以获得这两个阶段的人口演变信息;与传统框架不同,应对战略仅在环境变化阶段运行。关于简化,取向中心点战略被选定为新颖动态框架(FGERS-CPS)中的反应战略。FGERS-CPS不仅预测环境变化阶段所设定的最佳解决办法的变化趋势,而且预测环境静止阶段几代后人口演变趋势。与进向中心战略不同,一个简单的记忆战略和适应多样性维护战略被用于完整的FGERS-CPS 。在13个具有各种特点的实验性战略中,FGERS-CPS 展示了与传统战略的实验结果。