基于增强蜂群优化算法的特征选择算法

针对传统蜂群优化(BCO)算法探测能力强但搜索能力较弱的问题,提出一种搜索能力增强的BCO算法,并将其应用于数据特征选择问题以提高特征选择的性能。首先,为食物源引入全局权重的概念,用以评估各食物源对种群的重要性,降低蜂群搜索的随机性;然后,设计了两步筛选的招募方法提高蜂群搜索能力并保持多样性;最终,为食物源引入局部权重的概念,用于评估某个食物源与类标签的相关性,从而优化解特征选择问题。仿真实验结果表明,所提方法可以明显提高BCO的优化效果,同时获得了较好的特征选择效果,并且优于基于差异的人工蜂群算法(DisABC)和蜂群优化特征选择算法(BCOFS)。

Feature selection algorithms base on enhanced bee colony optimization algorithm

Concerning the problem that the traditional Bee Colony Optimization (BCO) has good exploration but week exploitation performance, an exploitation enhanced BCO algorithm was proposed, and applied to data feature selection problem in order to improve the performance of the feature selection. Firstly, global weight was introduced into the food source, and was used to evaluate the importance of each food source to population, thus the randomness of exploitation was reduced; then, a recruiting method with two-step filtering was designed to improve the exploitation performance and keep diversity; at last, local weight was introduced into the food source to evaluate the correlation between the food source and class labels which were used in the feature selection model. Simulation experimental results show that the proposed method can improve the effect of the BCO and get a good performance in the feature selection problem, and the method outperforms Dissimilarity based Artificial Bee Colony (DisABC) and Feature Selection based on Bee Colony Optimization (BCOFS).