A harmony search based gradient descent learning-FLANN (HS-GDL-FLANN) for classification

Abstract

The Harmony Search (HS) algorithm is meta-heuristic optimization inspired by natural phenomena called musical process and it quite simple due to few mathematical requirements and simple steps as compared to earlier meta-heuristic optimization algorithms. It mimics the local and global search procedure of pitch adjustment during production of pleasant harmony by musicians. Although HS has been used in many application like vehicle routing problems, robotics, power and energy etc., in this paper, an attempt is made to design a hybrid FLANN with Harmony Search based Gradient Descent Learning for classification. The proposed algorithm has been compared with FLANN, GA based FLANN and PSO based FLANN classifier to get remarkable performance. All the four classifier are implemented in MATLAB and tested by couples of benchmark datasets from UCI machine learning repository. Finally, to get generalized performance, 5 fold cross validation is adopted and result are analyzed under one-way ANOVA test.