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

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