A prediction strategy based on center points and knee points for evolutionary dynamic multi-objective optimization

A prediction strategy based on center points and knee points for evolutionary dynamic multi-objective optimization
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基于中心点和拐点的进化动态多目标优化预测策略

DOI:
10.1016/j.asoc.2017.08.004
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发表时间:
2017-12
影响因子:
8.7
通讯作者:
Jinhua Zheng
Jinhua Zheng
中科院分区:
计算机科学2区
文献类型:
--
作者:
Juan Zou;Qingya Li;Shengxiang Yang;Hui Bai;Jinhua Zheng

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在现实生活中,存在许多随时间变化的动态多目标优化问题,需要优化算法跟踪帕累托前沿(帕累托集)随时间的运动。在本文中,我们提出了一种新的基于中心点和膝点的预测策略(CKPS),该策略由三种机制组成。首先,提出了一种基于前视中心点的非支配集预测方法。其次,在预测种群中引入拐点集,准确预测环境变化后Pareto锋的位置和分布;最后,提出了一种自适应多样性维持策略,该策略可以根据问题的难易程度产生相应数量的随机个体来维持种群的多样性。将拟议的战略与其他四种最先进的战略进行比较。实验结果表明,该算法对进化动态多目标优化是有效的。
In real life, there are many dynamic multi-objective optimization problems which vary over time, requiring an optimization algorithm to track the movement of the Pareto front (Pareto set) with time. In this paper, we propose a novel prediction strategy based on center points and knee points (CKPS) consisting of three mechanisms. First, a method of predicting the non-dominated set based on the forward-looking center points is proposed. Second, the knee point set is introduced to the predicted population to predict accurately the location and distribution of the Pareto front after an environmental change. Finally, an adaptive diversity maintenance strategy is proposed, which can generate some random individuals of the corresponding number according to the degree of difficulty of the problem to maintain the diversity of the population. The proposed strategy is compared with four other state-of-the-art strategies. The experimental results show that CKPS is effective for evolutionary dynamic multi-objective optimization.
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