Please use this identifier to cite or link to this item:
https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/2298
Title: | Current-Best Particle Swarm Optimization |
Authors: | Medha, Ashmita Roy Biswas, Saroj Kr Gupta, Muskan Boruah, Arpita Nath Kumar, Rahul Verma, Vivek Purkayastha, Biswajit |
Keywords: | Cognitive Parameter Current-best Parameter ibest PSO Particle Swarm Optimization Social Parameter Swarm Intelligence |
Issue Date: | 2022 |
Publisher: | 2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2022 |
Citation: | pp. 1-6 |
Abstract: | Particle Swarm Optimization (PSO) is a metaheuristic optimization method based on swarm intelligence. Due to its flexibility and ability to produce optimum performance, it is commonly used in various applications. While PSO has been used extensively to provide solutions to various complicated problems in engineering, it has also many deficiencies. Several improved PSO techniques have been proposed to compensate these deficiencies. However, there are still some scopes of improvement in its components. In this work, we have proposed an improvised PSO called Current-best Particle Swarm Optimization (CPSO) which introduces a new parameter called 'cbest' that has been used in the social component of PSO to overcome the local minima issue. The suggested model, CPSO, has differentiate with the basic PSO method and the Iterative (ibest) PSO method using some optimization functions. The findings indicate that the recommended model outperforms the other models. © 2022 IEEE. |
URI: | https://doi.org/10.1109/IATMSI56455.2022.10119383 http://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/2298 |
ISBN: | 9781665477192 |
Appears in Collections: | Conference Papers |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.