论文标题
基于WGAN-GP的分布优化算法的多目标估计
Many-Objective Estimation of Distribution Optimization Algorithm Based on WGAN-GP
论文作者
论文摘要
分布算法（EDA）的估计是随机优化算法。 EDA建立了一个概率模型，可以通过统计学习方法从宏观的人群的角度来描述解决方案的分布，然后随机采样概率模型以生成新的人群。 EDA可以更好地解决多目标最佳问题（MOP）。但是，EDA的性能降低了解决多个目标的多目标最佳问题（MAOPS）。参考矢量引导的进化算法（RVEA）基于EDA框架可以更好地解决MAOPS。在我们的论文中，我们使用RVEA的框架。但是，我们通过Wasserstein生成对抗网络征收惩罚（WGAN-GP）产生了新的人群，而不是使用交叉和突变。 WGAN-GP具有快速收敛，良好稳定性和高样本质量的优势。 WGAN-GP学习从标准正态分布到基于给定数据集的给定数据集分布的映射关系。它可以迅速产生具有高度多样性和良好收敛性的人群。为了衡量性能，选择了RM-MEDA，MOPSO和NSGA-II以对DTLZ和LSMOP测试套件进行比较实验。
Estimation of distribution algorithms (EDA) are stochastic optimization algorithms. EDA establishes a probability model to describe the distribution of solution from the perspective of population macroscopically by statistical learning method, and then randomly samples the probability model to generate a new population. EDA can better solve multi-objective optimal problems (MOPs). However, the performance of EDA decreases in solving many-objective optimal problems (MaOPs), which contains more than three objectives. Reference Vector Guided Evolutionary Algorithm (RVEA), based on the EDA framework, can better solve MaOPs. In our paper, we use the framework of RVEA. However, we generate the new population by Wasserstein Generative Adversarial Networks-Gradient Penalty (WGAN-GP) instead of using crossover and mutation. WGAN-GP have advantages of fast convergence, good stability and high sample quality. WGAN-GP learn the mapping relationship from standard normal distribution to given data set distribution based on a given data set subject to the same distribution. It can quickly generate populations with high diversity and good convergence. To measure the performance, RM-MEDA, MOPSO and NSGA-II are selected to perform comparison experiments over DTLZ and LSMOP test suites with 3-, 5-, 8-, 10- and 15-objective.
