Function Optimization and Parameter Performance Analysis Based on Gravitation Search Algorithm

Algorithms

Jie-Sheng Wang¹  Jiang-Di Song¹ 

[1] School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114044, China;

关键词: gravitational search algorithm;    function optimization;    performance comparison;   

DOI  :  10.3390/a9010003

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.