基于分类的多策略预测方法求解动态多目标优化问题
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兰州理工大学

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TP273

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国家自然科学基金项目


A Classification-based Multi-Strategy Prediction Method for Dynamic Multi-Objective Optimization Problems
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Lanzhou University of Technology

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    摘要:

    实际生活中存在很多动态多目标优化问题(DMOPs),对此类问题,一旦环境发生了变化,就要求进化算法能快速地跟踪优化问题随时间移动的Pareto前沿(PF)或Pareto解集(PS)。本文提出了一种基于分类的多策略预测方法(CMSP)。首先,利用优化得到的近似最优解来检测PS的变化类型:不变、平移和其他;其次,针对不同的变化类型,采取不同的应对策略:若为不变,则保留精英个体,并保证多样性;若为平移,则对最优解集的中心点建立时间序列,通过预测梯度策略更新种群,然后,将预测的个体与从旧种群中保留下来的个体进行比较,以保证预测的准确性;若为其他,则对多个特殊点建立时间序列来预测新环境中个体的位置;最后,引入种群保留策略和记忆恢复策略,有利于更充分地利用历史信息。实验结果表明,CMSP可以很好地进行动态多目标优化。

    Abstract:

    There are many dynamic multi-objective optimization problems (DMOPs) in real life. Once the environment changes, evolutionary algorithms are required to quickly track the moving Pareto front (PF) or Pareto set (PS) of optimization problems over time. In this paper, we propose a classification-based multi-strategy prediction method (CMSP). Firstly, the approximate optimal solution obtained by optimization is used to detect the type of the PS change: invariance, translation and others. Secondly, different coping strategies are adopted for different types of change: if type is invariance, the elite individual is retained and diversity is ensured; if translation, a time series is established for the center point of the optimal solution set, and the population is updated by predictive gradient strategy, then, the predicted individuals are compared with the individuals retained from the old population to ensure the accuracy of the prediction; if others, several time series are established for multiple special points to predict the location of individuals in the new environment. Finally, introducing a population preservation strategy and a memory retrieval strategy is beneficial to more fully utilizing historical information. The experimental results show that CMSP can perform dynamic multi-objective optimization well.

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历史
  • 收稿日期:2019-09-19
  • 最后修改日期:2020-12-09
  • 录用日期:2020-01-18
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