ALO

基于CEC——2017benchmark测试集，计算最优 修正的果蝇算法，弥补原始果蝇算法在负数集上的缺失(modify fruit fly optimization)

根据Degree Centrality、Co-Expression Weighted by clustering Coefficient、Betweenness Centrality、PeC等六种参数的计算来寻找关键蛋白质(Find the key proteins according to the calculation of Degree Centrality, Co-Expression Weighted by clustering Coefficient, Betweenness Centrality, PeC and other six parameters.)

采用蚁群算法对BP神经网络进行优化，并结合实例进行应用验证。(The ant colony algorithm is used to optimize the BP neural network, and an example is used to validate it.)

说明：  基于变异策略的改进型花朵授粉算法，里边集成了粒子群算法、蝙蝠算法以及定向变异策略，进行对比(An improved flower pollination algorithm based on mutation strategy, which integrates particle swarm optimization, bat algorithm and directional mutation strategy, is compared.)

说明：  解决车间资源分配的动态调度问题，采用遗传算法(Solving the dynamic scheduling problem of workshop resource allocation, using genetic algorithm)

标准的PSO算法以及几个典型测试函数的matlab源程序，资料可供大家参考(Standard PSO algorithm and several typical test functions of Matlab source code, information for your reference.)

原始的最优化算法，用来求解最小值最大值，适合新手入门学习(fruit fly optimization)

利用遗传算法求解柔性车间调度问题，采用Python编程实现(Using Genetic Algorithm to Solve Flexible Workshop Scheduling Problem)

灰狼算法和粒子群算法的结合，是两种种群算法的结合，值得一看(The combination of grey Wolf algorithm and particle swarm optimization is a combination of two population algorithms, which is worth a look)

说明：  标准萤火虫算法,程序运行稳定，可以自测！！！！！！！(Standard firefly algorithm, the program runs stably and can be self-tested! ! ! ! ! ! !)