Automatic differentiation based for particle swarm optimization Steepest descent direction

International Journal of Advances in Intelligent Informatics

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Field Value
 
Title Automatic differentiation based for particle swarm optimization Steepest descent direction
 
Creator Thobirin, Aris
Yanto, Iwan Tri Riyadi
 
Subject Particle swam optimization; Gradient; Descent direction;AD
 
Description Particle swam optimization (PSO) is one of the most effective optimization methods to find the global optimum point. In other hand, the descent direction (DD) is the gradient based method that has the local search capability. The combination of both methods is promising and interesting to get the method with effective global search capability and efficient local search capability. However, In many application, it is difficult or impossible to obtain the gradient exactly of an objective function. In this paper, we propose Automatic differentiation (AD) based for PSODD. we compare our methods on benchmark function. The results shown that the combination methods give us a powerful tool to find the solution.
 
Publisher Universitas Ahmad Dahlan
 
Contributor
 
Date 2015-07-31
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Format application/pdf
 
Identifier http://ijain.org/index.php/IJAIN/article/view/29
10.26555/ijain.v1i2.29
 
Source International Journal of Advances in Intelligent Informatics; Vol 1, No 2 (2015): July 2015; 90-97
2548-3161
2442-6571
 
Language eng
 
Relation http://ijain.org/index.php/IJAIN/article/view/29/ijain_v1i2_p90_97
 
Rights Copyright (c) 2015 International Journal of Advances in Intelligent Informatics
http://creativecommons.org/licenses/by-sa/4.0
 

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