全局混沌蝙蝠优化算法

为提高蝙蝠算法进行特征选择的正确率，提出全局混沌蝙蝠优化算法(GCBA).首先，GCBA采用混沌映射方法使种群的初始化能够遍历整个解空间，获取蝙蝠初始的最优位置，使其具有更加丰富的种群，解决了初始化种群随机性的问题.同时，GCBA引入当前粒子的最优解和当前种群的最优解跳出局部最优解，可有效避免算法早熟，有利于提高算法的全局搜索能力.蝙蝠算法(BA)、粒子群算法(PSO)与遗传算法(GA)在10个数据集上的测试结果表明，所提算法具有更高的分类精度和更强的跳出局部最优的能力.

Global Chaotic Bat Optimization Algorithm

In order to improve the accuracy of feature selection of bat algorithm， a global chaotic bat optimization algorithm(GCBA) was proposed. Firstly， GCBA adopts chaotic mapping method to enable the initialization of the population to traverse the entire solution space， obtain the optimal position of the bat， and make it more abundant. It solved the problem of initial population randomness. At the same time， GCBA introduces the optimal solution of the current particle and the optimal solution of the current population to jump out of local optimal solutions， which can effectively avoid the premature and improve the global search ability of the algorithm. The results of the bat algorithm(BA)， particle swarm optimization(PSO) and genetic algorithm(GA) on 10 data sets showed that the proposed algorithm has higher classification accuracy and stronger ability to jump out of local optimum.