International Journal of Mathematical Sciences and Computing (IJMSC)
IJMSC Vol. 8, No. 2, 8 Jun. 2022
Cover page and Table of Contents: PDF (size: 518KB)
Adaptive Social Acceleration Constant Based Particle Swarm Optimization
PDF (518KB), PP.28-36
Views: 0 Downloads: 0
Author(s)
Jyoti Jain
1,*
Uma Nangia
2
N. K. Jain
2
Index Terms
Convergence, Inertia weight, Optimization, Population, Particles, Social acceleration constant.
Abstract
In this paper, an attempt has been made to develop an Adaptive Social Acceleration Constant based PSO (ASACPSO). ASACPSO converge faster in comparison to basic PSO. The best value has been selected based on the minimum number of kounts required to minimize the function. Adaptive Social Acceleration Constant based PSO (ASACPSO) has been developed using the best value of adaptive social acceleration constant. The Adaptive Social Acceleration Constant has been searched using three formulations which led to the development of three algorithms-ALDPSO, AELDPSO-I and AELDPSO-II. All three were implemented on Rosenbrock function to get the best value of adaptive social acceleration constant. Similarly it has been implemented on seven mathematical benchmark functions and its performance has been compared to Basic Particle Swarm Optimization (BPSO). ASACPSO was observed to converge faster and give better accuracy. Results show that Kounts required for convergence of mathematical function is lesser for ASACPSO in comparison to basic PSO.ASACPSO reduces the computational time to optimize the function.
Cite This Paper
Jyoti Jain, Uma Nangia, N. K. Jain," Adaptive Social Acceleration Constant Based Particle Swarm Optimization ", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.8, No.2, pp. 28-36, 2022. DOI: 10.5815/ijmsc.2022.02.03
Reference
[1]J. Kennedy and R. Eberhart, “Particle swarm optimization”, Proc. IEEE International Conference on Neural networks (Perth, Australia), 1995, IEEE Service Center, Piscataway, NJ, pp. 1942-1948.
[2]R. Poli, J. Kennedy, and T. Blackwell, “Particle swarm optimization. An overview,” Swarm Intelligence, vol. 1, no. 1, pp. 33–57, 2007.
[3]Dian Palupi Rini, Siti Mariyam Shamsuddin, and Siti Sophiyati Yuhaniz, “Particle Swarm Optimization: Technique System and Challenges”, International Journal of Computer Applications (0975-8887) Vol-14 – January 1, 2011.
[4]Jain N.K., Nangia Uma, Jyoti Jain, “A Review of Particle swarm optimization”, Journal of The Institution of Engineers (India ): Series B, August, 2018, 99(4), pp.407-411. ISSN 2250-2106, DOI 10.1007/s40031-018-0323-y.
[5]Russell C. Eberhart, and yuhui shi, “Particle Swarm Optimization: Developments, Applicationsand Resources”Evolutionary Computation,2001 Proceeding of the 2001 Congress on IEEE,1(2001),pp.81-86
[6]Jaco F. Schutteand Albert A.Groenwold, “A Study of Global Optimization Using Particle Swarms”, Journal of Global Optimization, Vol. 31, Springer 2005, pp. 93-108.
[7]yitaka Maeda and Naoto Matsushita , “Empirical Study of Simultaneous Perturbation Particle Swarm Optimization”, SICE Annual Conference, August 20-22, 2008, pp. 2545-2548.
[8]Zhi-Xiang Hou, “Wiener model identification based on adaptive particle swarm optimization”, Proceedings of the Seventh IEEE International Conference on Machine Learning and Cybernetics, Kunming, 12-15 July 2008, pp. 1041-1045.
[9]Md. Sakhawat Hossen, Fazle Rabbi, and Md. Mainur Rahman, “Adaptive Particle Swarm Optimization(APSO) for multimodal function optimization”, International Journal of Engineering and Technology, Vol.1, No. 3,2009, pp. 98-103.
[10]N.K.Jain, Uma Nangia, Jyoti Jain “An improved PSO based on initial selection of particles (ISBPSO) for Economic Load Dispatch” Proceedings of 1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems(ICPEICES 2016),4-6 july ,2016.
[11]N.K.Jain, Uma Nangia, Jyoti Jain, “An improved PSO based on initial selection of particles (ISBPSO)”, Proceedings of 1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems(ICPEICES 2016),4-6 july ,2016.
[12]N.K.Jain, Uma Nangia, Ashwary jain “PSO for Multiobjective Economic Load Dispatch(MELD) for minimizing Generation Cost and Transmission Losses”, journal of the Institution of Engineers (India):series B,2015.
[13]Jain N.K., Nangia Uma, Jyoti Jain, “Multiobjective Economic Load Dispatch Studies in 2-D and 3-D Space by Particle Swarm Optimization Technique”, February, 2019, Journal of The Institution of Engineers (India ): Springer Series B, DOI : 10.1007/s40031-019-00386-z.
[14]Jain N.K., Nangia Uma, Jyoti Jain, “ Economic load Dispatch using Adaptive Social Acceleration Constant based Particle Swarm Optimization”, Journal Of Institution Of Engineers (India): Series B, October 2018, 99(5), 431-439, ISSN 2250-2106, DOI 10.1007/s40031-018-0322-z.
[15]N. K. Jain, Uma Nangia, Jyoti Jain, “Impacts of PSO Parameters on its Convergence” IEEE second International Conference on Power Electronics, Intelligent Control and Energy Sysytems (ICPEICES-2018), Delhi, pp.1-5, (22-24 October, 2018).
[16]Shengli Song, Li Kong and Jingjing Cheng, A Novel Particle Swarm Optimization Algorithm Model with Centroid and its Application, I.J. Intelligent Systems and Applications, 2009, 1, 42-49 October 2009 in MECS(http://www.mecs-press.org/)
[17]Sudip Mandal, “ A Modified Particle Swarm Optimization Algorithm based on Self-Adaptive Acceleration Constants” I.J. Modern Education and Computer Science, 2017, 8, 49-56 Published Online August 2017 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijmecs.2017.08.07