WebJan 31, 2024 · Dragonfly algorithm (DA) is a novel swarm intelligence proposed by Mirjalili in 2015 for solving discrete problems, single-objective problems, and multi-objective problems [ 86 ]. Swarm intelligence is driven by animal populations and social bugs. We would like to show you a description here but the site won’t allow us. WebMay 22, 2024 · Download. Overview. Functions. Version History. Reviews (2) Discussions (4) This is the binary version of the recently proposed DA algorithm. The main …
(PDF) Dragonfly algorithm: a comprehensive survey of its results ...
WebJan 5, 2024 · Dragonfly algorithm (DA) is a recently established algorithm inspired by the swarming patterns of dragonflies in nature. It is originally designed to solve continuous optimization tasks. Later on, Mirjalili proposed a binary version of the dragonfly algorithm (BDA), which can be used to solve discrete problems (e.g. feature selection) [17]. WebJan 24, 2024 · Wavelength selection is an important preprocessing issue in near-infrared (NIR) spectroscopy analysis and modeling. Swarm optimization algorithms (such as genetic algorithm, bat algorithm, etc.) have been successfully applied to select the most effective wavelengths in previous studies. However, these algorithms suffer from the problem of … church purchase request form
Comparison of Binary Particle Swarm Optimization And Binary Dragonfly ...
WebAbstract: Feature selection is an effective method to eliminate irrelevant, redundant and noisy features, which improves the performance of classification and reduces the computational burden in machine learning. In this paper, an improved binary dragonfly algorithm (IBDA) which extends from the conventional dragonfly algorithm (DA) is … WebJan 31, 2024 · DA is considered one of the promising swarm optimization algorithms because it successfully applied in a wide range of optimization problems in several fields, … WebJan 9, 2024 · Binary Dragonfly Algorithm for Feature Selection Introduction This toolbox offers Binary Dragonfly Algorithm ( BDA ) method The Main file illustrates the example of how BDA can solve the feature selection problem using benchmark data-set. Input feat : feature vector ( Instances x Features ) label : label vector ( Instances x 1 ) dewinter consulting