The techniques that are used for finding the optimal solution around the feasible solutions are called Heuristics. The algorithms which mimic the natural systems are called as Heuristic Algorithms. These algorithms have the properties of working in a parallel manner and information interchange among the agents. They are probabilistic in nature and do not have central control.
Published in Chapter:
Analysis of Gravitation-Based Optimization Algorithms for Clustering and Classification
Sajad Ahmad Rather (Pondicherry University, India) and P. Shanthi Bala (Pondicherry University, India)
Copyright: © 2020
|Pages: 26
DOI: 10.4018/978-1-7998-0106-1.ch005
Abstract
In recent years, various heuristic algorithms based on natural phenomena and swarm behaviors were introduced to solve innumerable optimization problems. These optimization algorithms show better performance than conventional algorithms. Recently, the gravitational search algorithm (GSA) is proposed for optimization which is based on Newton's law of universal gravitation and laws of motion. Within a few years, GSA became popular among the research community and has been applied to various fields such as electrical science, power systems, computer science, civil and mechanical engineering, etc. This chapter shows the importance of GSA, its hybridization, and applications in solving clustering and classification problems. In clustering, GSA is hybridized with other optimization algorithms to overcome the drawbacks such as curse of dimensionality, trapping in local optima, and limited search space of conventional data clustering algorithms. GSA is also applied to classification problems for pattern recognition, feature extraction, and increasing classification accuracy.