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Algorithms 2016, 9(1), 3; doi:10.3390/a9010003

Function Optimization and Parameter Performance Analysis Based on Gravitation Search Algorithm

1
School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114044, China
2
National Financial Security and System Equipment Engineering Research Center, University of Science and Technology Liaoning, Anshan 114044, China
*
Author to whom correspondence should be addressed.
Academic Editor: Paul M. Goggans
Received: 10 October 2015 / Revised: 1 December 2015 / Accepted: 21 December 2015 / Published: 24 December 2015
View Full-Text   |   Download PDF [1545 KB, uploaded 28 December 2015]   |  

Abstract

The gravitational search algorithm (GSA) is a kind of swarm intelligence optimization algorithm based on the law of gravitation. The parameter initialization of all swarm intelligence optimization algorithms has an important influence on the global optimization ability. Seen from the basic principle of GSA, the convergence rate of GSA is determined by the gravitational constant and the acceleration of the particles. The optimization performances on six typical test functions are verified by the simulation experiments. The simulation results show that the convergence speed of the GSA algorithm is relatively sensitive to the setting of the algorithm parameters, and the GSA parameter can be used flexibly to improve the algorithm’s convergence velocity and improve the accuracy of the solutions. View Full-Text
Keywords: gravitational search algorithm; function optimization; performance comparison gravitational search algorithm; function optimization; performance comparison
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Wang, J.-S.; Song, J.-D. Function Optimization and Parameter Performance Analysis Based on Gravitation Search Algorithm. Algorithms 2016, 9, 3.

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