Particle swarm optimization- based support vector regression for predictions: Approach and applications
description: For centuries, people have drawn inspiration from nature, and there is always more to learn and discover. The Particle Swarm Optimization (PSO) algorithm, a stochastic optimization algorithm based on population and inspired by the intelligent collective behavior of certain animals like fish schools or flocks of birds, is one of the most well-known nature-inspired algorithms presented in this work. As more was known about the fundamentals of this methodology, researchers produced new iterations to satisfy varying needs, new applications in diverse domains, theoretical research on the effects of different parameters, and a multitude of algorithm variations. PSO-support vector regression (SVR) is one such variant of this algorithm. SVR is a kind of Support Vector Machine (SVM) that solves regression problems. It seeks to identify a function that diverges from the actual values observed by no more than a given margin. The main idea is to retain the error under a certain threshold. PSO optimizes SVR parameters, including regularization, epsilon, and kernel parameters. This combination takes advantage of the strengths of both approaches. In this chapter, we will discuss the importance of the PSO-SVR algorithm in predicting the outcomes of real-world applications classified as healthcare, environmental, industrial, commercial, smart city, and other broad applications. 2025 selection and editorial matter, Sulabh Bansal, Aprna Tripathi, Shilpa Srivastava and Prem Prakash Vuppuluri; individual chapters, the contributors.
creator: Anand, Garima; Srivastava, Shilpa; Shandilya, Anish; Singh, Garima; Tripathi, Aprna
source: Nature-inspired Metaheuristic Algorithms: Solving Real World Engineering Problems;pp.343-359
publisher: CRC Press
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 978-104034487-3; 978-103277087-1;
language: English
type: Book chapter
coverage: Anand G., Christ University Bengaluru, India; Srivastava S., Christ University Bengaluru, India; Shandilya A., Samatrix consulting, Gurgaon, India; Singh G., Inderprastha Engineering College, Ghaziabad, India; Tripathi A., Department of Data Science and Engineering, Manipal University Jaipur, Jaipur, India