Open AccessGrey wolf optimizer and other metaheuristic optimization techniques with image processing as their applications: a review
Author(s) -
Abhishek Kumar,
Lekhraj,
Safalata Singh,
Amit Kumar
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1136/1/012053
Subject(s) - metaheuristic , ant colony optimization algorithms , parallel metaheuristic , computer science , particle swarm optimization , image processing , meta optimization , genetic algorithm , image segmentation , artificial intelligence , artificial bee colony algorithm , field (mathematics) , multi swarm optimization , mathematical optimization , image (mathematics) , algorithm , machine learning , mathematics , pure mathematics
Image processing is an evolutionary field in the domain of computer vision that currently has a comprehensive spectrum of applications. It is being employed in image segmentation, image classification, medical imaging, image compression, etc. A lot of real-world prominent issues are tackling employed through these application techniques. These techniques can be employed by means of various algorithms; however, these offered immensely dominant outcomes with existing and modified optimization algorithms. Some of the metaheuristic optimization algorithms applied during the above techniques include Ant Colony Optimization (ACO), Genetic Algorithm (GA), Bat Algorithm (BA), Grey Wolf Optimizer (GWO), Evolutionary Strategy (ES), Particle Swarm Optimization (PSO), Genetic Programming (GP), and so forth. Hence, this manuscript’s main objective is that the study of several applied optimization algorithms and their variants thus lead to the various domain of image processing concludes it work more efficiently and robustly.