Method and device for optimizing FKNN model parameters based on variation Sashimi swarm algorithm

The invention provides a method for optimizing parameters of a fuzzy k-nearest neighbor (FKNN) model based on a variation gall sea squirt swarm algorithm. The method comprises the following steps: acquiring sample data and normalizing the acquired sample data; optimizing the parameters k, m of the FKNN by using an integrated variation Sashimi swarm algorithm of a preset restart mechanism; and optimizing the FKNN model by using the optimal neighbor number k and the fuzzy intensity coefficient m value, and predicting the test data based on 10-fold cross validation. By implementing the method, the convergence speed and convergence precision of the algorithm can be improved, and the ability of the algorithm to escape from the local optimal solution is improved, so that a better global approximate optimal solution is found. 本发明提供一种基于变异樽海鞘群算法优化模糊k近邻(FKNN)模型参数的方法，包括获取样本数据并对所获取到的样本数据进行归一化处理；利用预设的重启机制的集成变异樽海鞘群算法优化FKNN的参数k,m；使用最优的近邻个数k和模糊强度系数m值来优化FKNN模型，并基于10折交叉验证对测试数据进行预测。实施本发明，能提升算法的收敛速度和收敛精度，提升算法逃脱局部最优解的能力，从而找到更优的全局