基于差分进化生物地理学优化的多层感知器训练方法

针对生物地理学优化训练多层感知器存在的早熟收敛以及初始化灵敏等问题，提出一种基于差分进化生物地理学优化的多层感知器训练方法。将生物地理学优化（Biogeography-based Optimization，BBO）与差分进化(Differential Evolution，DE)算法相结合，形成改进的混合DE_BBO算法；采用改进的DE_BBO来训练多层感知器（Multi-Layer Perceptron，MLP），并应用于虹膜、乳腺癌、输血、钞票验证等4类数据分类。与BBO、PSO、GA、ACO、ES、PBIL等6种主流启发式算法的实验结果进行比较表明，DE_BBO_MLP算法在分类精度和收敛速度等方面优于已有方法。

Multi-Layer Perceptron Using Hybrid Differential Evolution and Biogeography-based Optimization

The problems of premature convergence and initialization-sensitive are often experiencing when we train the Multi-Layer Perceptron using the biogeography-based optimization. This paper proposed a novel Multi-Layer Perceptron training method using hybrid differential evolution and biogeography-based optimization. This paper introduces the differential evolution to the biogeography-based optimization to construct the hybrid DE_BBO algorithm and then uses the hybrid DE_BBO algorithm for training MLPs. In order to investigate the efficiencies of DE_BBO in training MLPs, this paper employed four classification datasets, including the Iris dataset, the Breast cancer dataset, the Blood Transfusion datasets and the Banknote authentication dataset. Comparing with six well-known heuristic algorithms, including BBO, PSO, GA, ACO, ES, and PBIL in a statistically significant way, the experimental results show that training MLPs using hybrid DE_BBO is significantly better than the current heuristic learning algorithms in terms of convergence speed and convergence accuracy.