Automatic differentiation based for particle swarm optimization Steepest descent direction

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 me...

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Bibliographic Details
Published in:International journal of advances in intelligent informatics 2015-07, Vol.1 (2), p.90-97
Main Authors: Thobirin, Aris, Yanto, Iwan Tri Riyadi
Format: Article
Language:English
Subjects:
Descent direction
Gradient
Particle swam optimization
Online Access:Get full text
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Summary: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.
ISSN:2442-6571
2442-6571
DOI:10.26555/ijain.v1i2.29