Research
A wrapper-filter feature selection technique based on ant colony optimization
Feature Selection
Ant Colony Optimization (ACO) is a widely used wrapper-based meta-heuristic optimization algorithm. We have modified the inner structure of ACO and replaced the pheromone update process with a filter method to speed up its searching capabilities. We have achieved significant improvement both in terms of classification accuracy for feature selection (FS) and time requirement over a number of UCI datasets.
Paper / BibTex
Feature Selection
Ant Colony Optimization (ACO) is a widely used wrapper-based meta-heuristic optimization algorithm. We have modified the inner structure of ACO and replaced the pheromone update process with a filter method to speed up its searching capabilities. We have achieved significant improvement both in terms of classification accuracy for feature selection (FS) and time requirement over a number of UCI datasets.
Paper / BibTex
Groundwater Flow Algorithm: A Novel Hydrogeology based Optimization Algorithm
Numerical Optimization
Groundwater Flow Algorithm (GWFA) is a novel meta-heuristic optimization algorithm inspired from the movement of groundwater from recharge areas to discharge areas. It follows a position update procedure guided by Darcy’s law which provides a mathematical framework of groundwater flow. It has been applied over various test problems and engineering design problems where it has outperformed numerous other optimization algorithm.
Preprint / BibTex
Numerical Optimization
Groundwater Flow Algorithm (GWFA) is a novel meta-heuristic optimization algorithm inspired from the movement of groundwater from recharge areas to discharge areas. It follows a position update procedure guided by Darcy’s law which provides a mathematical framework of groundwater flow. It has been applied over various test problems and engineering design problems where it has outperformed numerous other optimization algorithm.
Preprint / BibTex
Enhancement of image contrast using Selfish Herd Optimizer
Image Contrast Enhancement
Image Contrast enhancement (ICE) is an important pre-processing task in any Image Analysis (IA) system. In this paper, we formulate the image contrast enhancement problem as an optimization problem where the goal is to optimize the pixel intensity values of an input image to obtain a contrast enhanced version of the same. This optimization task is executed by suitably customizing a nature-inspired optimization algorithm called Selfish Herd Optimizer (SHO). The optimization problem is solved using two different solution representations: pixel wise optimization (SHO (direct)) and transformation function based optimization (SHO (transformation)).
Paper / BibTex
Image Contrast Enhancement
Image Contrast enhancement (ICE) is an important pre-processing task in any Image Analysis (IA) system. In this paper, we formulate the image contrast enhancement problem as an optimization problem where the goal is to optimize the pixel intensity values of an input image to obtain a contrast enhanced version of the same. This optimization task is executed by suitably customizing a nature-inspired optimization algorithm called Selfish Herd Optimizer (SHO). The optimization problem is solved using two different solution representations: pixel wise optimization (SHO (direct)) and transformation function based optimization (SHO (transformation)).
Paper / BibTex
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