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Cooperating swarms: A paradigm for collective intelligence and its application in finance

by Sumona Mukhopadhyay, Santo Banerjee
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 6 - Number 10
Year of Publication: 2010
Authors: Sumona Mukhopadhyay, Santo Banerjee
10.5120/1107-1450

Sumona Mukhopadhyay, Santo Banerjee . Cooperating swarms: A paradigm for collective intelligence and its application in finance. International Journal of Computer Applications. 6, 10 ( September 2010), 31-41. DOI=10.5120/1107-1450

@article{ 10.5120/1107-1450,
author = { Sumona Mukhopadhyay, Santo Banerjee },
title = { Cooperating swarms: A paradigm for collective intelligence and its application in finance },
journal = { International Journal of Computer Applications },
issue_date = { September 2010 },
volume = { 6 },
number = { 10 },
month = { September },
year = { 2010 },
issn = { 0975-8887 },
pages = { 31-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume6/number10/1107-1450/ },
doi = { 10.5120/1107-1450 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:55:05.329471+05:30
%A Sumona Mukhopadhyay
%A Santo Banerjee
%T Cooperating swarms: A paradigm for collective intelligence and its application in finance
%J International Journal of Computer Applications
%@ 0975-8887
%V 6
%N 10
%P 31-41
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The control of nonlinear chaotic system and the estimation of parameters is a vital issue in nonlinear science. Studies on parameter estimation for chaotic systems have been investigated recently. A variant of Particle Swarm Optimization (PSO) known as Chaotic Multi Swarm Particle Swarm Optimization (CMS-PSO) is proposed which is inspired from the metaphor of ecological co-habitation of species. The generic PSO is modified with the chaotic sequences for multi-dimension parameter estimation and optimization by forming multiple cooperating swarms. Results demonstrate the effectiveness of the scheme in successfully estimating the unknown parameters of a new hyperchaotic finance system. Numerical results and comparison demonstrate that for the given parameters of the nonlinear system, CMS-PSO can identify the optimized parameters effectively to reach the pareto optimal solution and convergence speed.

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Index Terms

Computer Science
Information Sciences

Keywords

Computational intelligence particle swarm optimization Finance system chaos multi-objective global optimization

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