基于个体预测的动态多目标优化算法

Dynamic multi-objective optimization algorithm based on individual prediction

为了快速追踪随环境变化的动态多目标优化问题的Pareto前沿,提出基于个体预测的动态多目标优化算法(IPS).利用参考点联系算法筛选出特殊点,该特殊点具有良好的收敛性和多样性,通过对特殊点集的预测快速响应环境变化.提出针对种群中心点预测的反馈校正机制,在预测非支配解集的过程中,对预测步长进行反馈校正,从而使预测更加准确;为了避免算法陷入局部最优,提出混合多样性维持机制,引入由拉丁超立方抽样和精度可控的突变策略分别产生的随机个体,以提高种群的多样性.将所提算法与其他4种动态多目标优化算法进行对比分析,实验结果表明,IPS能够平衡种群的多样性和收敛性,在FDA、DMOP、F5~F10系列问题上,实验结果优于其他4种算法.

A dynamic multi-objective optimization algorithm based on individual prediction(IPS)was proposed to quickly track the Pareto optimal front of the dynamic multi-objective optimization problem that changed with the environment.Firstly,the special points with good convergence and diversity were selected by the reference point relation algorithm,and the environment changes can be quickly responded to by predicting the special points set.Secondly,a feedback correction mechanism for population center point predication was proposed,and in the process of predicting the non-dominant solution set,the prediction step size was corrected to make the prediction more accurate.Finally,to avoid the algorithm falling into local optimal,a hybrid diversity maintenance mechanism was proposed,which introduced random individuals generated by Latin hypercube sampling and a precision controllable mutation strategy to improve the diversity of the population.The proposed algorithm was compared with the other four dynamic multi-objective optimization algorithms.Experimental results show that IPS can balance the diversity and convergence of the population,and the experimental results are better than that of the other four algorithms on the FDA,DMOP,and F5~F10 test suite.