Hyperspectral images (HSI) have recently been exploited in several aspects as they contain many contiguous narrow spectral informative-rich bands. The curse of dimensionality in hyperspectral images is an essential challenge as it possesses plenty of redundant bands that lead to the Hughes phenomenon. However, many feature selection or band selection techniques have been performed for dimensionality reduction of HSI. In this manuscript, firstly, a novel approach for spectral bands selection process is presented for hyperspectral images dimensionality reduction using Krill Herd (KH) Algorithm. However, KH is a heuristic search method that seeks to reach the optimum global solution within the search space and evade falling into the local optima. KH relies on simulating the herding behavior of krill in the sea in order to determine the most informative and relevant bands. Secondly, an Edge-preserving filter (EPF) was utilized to extract the spatial characteristics while reducing noise and obtaining a suitable smoothing that improves the performance of the classification process. Finally, the support vector machine (SVM) classifier at pixel level was performed for the classification of HSI. Moreover, the proposed work was compared to the Harmony Search (HS), Genetic Algorithm (GA), Bat Algorithm (BA), Particle Swarm Optimization (PSO), and Firefly Algorithm (FA). In addition, the classification results for overall accuracy (OA) on four popular publicly datasets were 96.54%, 98.93%, 99.78%, and 98.66% for the Indian Pines scene, the Pavia University scene, the Salinas scene, and the Botswana scene, respectively.