结合头脑风暴优化的混合蚁群优化算法

特征选择能够有效提升数据分类的性能。为了进一步提升蚁群优化（ACO）在特征选择上的求解能力，提出一种结合头脑风暴优化的混合蚁群优化（ABO）算法。该算法利用信息交流档案维护历史较好解，并通过基于松弛因子的时间最久优先方法动态更新档案。当ACO的全局最优解多次未更新时，采用基于Fuch混沌映射方法的路径-想法转换算子将档案中的路径解转换为想法解，并将其作为初始种群，通过头脑风暴优化（BSO）在更广阔的空间中搜索较好解。对所提算法在6组典型的二分类数据集上进行实验，分析了其参数敏感性，并与混合萤火虫粒子群优化（HFPSO）算法、粒子群优化与引力搜索算法（PSOGSA）以及遗传算法（GA） 这三种典型的演化算法进行对比。实验结果表明，相较于对比算法，所提算法在分类正确率上至少可提高2.88%~5.35%，在F1指标上至少可提高0.02~0.05，验证了所提算法的有效性和优越性。

Hybrid ant colony optimization algorithm with brain storm optimization

Feature selection can improve the performance of data classification effectively. In order to further improve the solving ability of Ant Colony Optimization (ACO) on feature selection, a hybrid Ant colony optimization with Brain storm Optimization (ABO) algorithm was proposed. In the algorithm, the information communication archive was used to maintain the historical better solutions, and a longest time first method based on relaxation factor was adopted to update archive dynamically. When the global optimal solution of ACO was not updated for several times, a route-idea transformation operator based on Fuch chaotic map was used to transform the route solutions in the archive to the idea solutions. With the obtained solutions as initial population, the Brain Storm Optimization (BSO) was adopted to search for better solutions in wider space. On six typical binary datasets, experiments were conducted to analyze the sensibility of parameters of the proposed algorithm, and the algorithm was compared to three typical evolutionary algorithms:Hybrid Firefly and Particle Swarm Optimization (HFPSO) algorithm, Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA) and Genetic Algorithm (GA). Experimental results show that compared with the comparison algorithms, the proposed algorithm can improve the classification accuracy by at least 2.88% to 5.35%, and the F1-measure by at least 0.02 to 0.05, which verify the effectiveness and superiority of the proposed algorithm.