TY - GEN
T1 - A new hybrid firefly algorithm for complex and nonlinear problem
AU - Abdullah, Afnizanfaizal
AU - Deris, Safaai
AU - Mohamad, Mohd Saberi
AU - Hashim, Siti Zaiton Mohd
PY - 2012
Y1 - 2012
N2 - Global optimization methods play an important role to solve many real-world problems. However, the implementation of single methods is excessively preventive for high dimensionality and nonlinear problems, especially in term of the accuracy of finding best solutions and convergence speed performance. In recent years, hybrid optimization methods have shown potential achievements to overcome such challenges. In this paper, a new hybrid optimization method called Hybrid Evolutionary Firefly Algorithm (HEFA) is proposed. The method combines the standard Firefly Algorithm (FA) with the evolutionary operations of Differential Evolution (DE) method to improve the searching accuracy and information sharing among the fireflies. The HEFA method is used to estimate the parameters in a complex and nonlinear biological model to address its effectiveness in high dimensional and nonlinear problem. Experimental results showed that the accuracy of finding the best solution and convergence speed performance of the proposed method is significantly better compared to those achieved by the existing methods.
AB - Global optimization methods play an important role to solve many real-world problems. However, the implementation of single methods is excessively preventive for high dimensionality and nonlinear problems, especially in term of the accuracy of finding best solutions and convergence speed performance. In recent years, hybrid optimization methods have shown potential achievements to overcome such challenges. In this paper, a new hybrid optimization method called Hybrid Evolutionary Firefly Algorithm (HEFA) is proposed. The method combines the standard Firefly Algorithm (FA) with the evolutionary operations of Differential Evolution (DE) method to improve the searching accuracy and information sharing among the fireflies. The HEFA method is used to estimate the parameters in a complex and nonlinear biological model to address its effectiveness in high dimensional and nonlinear problem. Experimental results showed that the accuracy of finding the best solution and convergence speed performance of the proposed method is significantly better compared to those achieved by the existing methods.
KW - biological model
KW - Differential Evolution
KW - Firefly Algorithm
KW - hybrid optimization
KW - parameter estimation
UR - http://www.scopus.com/inward/record.url?scp=84864320135&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84864320135&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-28765-7_81
DO - 10.1007/978-3-642-28765-7_81
M3 - Conference contribution
AN - SCOPUS:84864320135
SN - 9783642287640
T3 - Advances in Intelligent and Soft Computing
SP - 673
EP - 680
BT - Distributed Computing and Artificial Intelligence - 9th International Conference
T2 - 9th International Conference on Distributed Computing and Artificial Intelligence, DCAI 2012
Y2 - 28 March 2012 through 30 March 2012
ER -