差分进化樽海鞘群特征选择算法

针对樽海鞘群优化算法(SSA: Salp Swarm Algorithm)在求解特征选择问题时存在易陷入局部最优、收敛速度慢的不足,基于樽海鞘群优化算法提出了新的改进算法差分进化樽海鞘群特征选择算法(DESSA:Differential Evolution Salp Swarm Algorithm).DESSA中采用了差分进化策略替代平均算子作为新的粒子迁移方式以增强搜索能力,并加入进化种群动态机制(EPD: Evolution Population Dynamics),加强收敛能力.实验中以KNN(K-Nearest Neighbor)分类器作为基分类器,以UCI(University of California Irvine)数据库中的8组数据集作为实验数据,将DESSA与SSA同具有代表性的算法进行对比实验,实验结果表明,DESSA算法各考察指标较原算法有明显提升,较其他算法相对优越.

Differential Evolutionsalp Salp Swarm Feature Selection Algorithm

Aiming at the shortcomings of Salp Swarm Algorithm (SSA: Salp Swarm Algorithm) that are easy to fall into local optimality and slow convergence when solving feature selection problems, Based on salp swarm optimization algorithm, its improved version, differential evolution salp swarm feature selection algorithm(DESSA: Differential Evolution Salp Swarm Algorithm) is proposed. Differential evolution strategy is applied to replace the ordinary operator as the new way of moving particles to enhance search capabilities. And evolutionary population dynamics ( EPD: Evolution Population Dynamics) is proposed to enhance convergence efficiency.Utilizing K-nearest neighbor (KNN: K-Nearest Neighbor) as classifier and eight datasets come from the UCI (University of California Irvine) machine learning repository, DESSA is compared with the SSA and other high performing approaches proposed recently. The experimental result confirms the efficiency of DESSA in improving the SSA in several respects and its ability to better solve the problem of feature selection compared with other approaches of feature selection.