A hybrid adaptive cuckoo search optimization algorithm for the problem of chaotic systems parameter estimation. Issue 6 (August 2016)

• Record Type: Journal Article
• Authors: Wang, Jun; Zhou, Bihua
• Is Part Of: Neural computing & applications. Volume 27:Issue 6(2016)
• Page Start: 1511
• Page End: 1517
• Publication Date: 2016-08
• Subjects: Cuckoo search algorithm -- Adaptive operation -- Orthogonal design -- Chaotic system -- Parameter estimation; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1949-1
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract This paper introduces a novel hybrid adaptive cuckoo search (HACS) algorithm to establish the parameters of chaotic systems. In order to balance and enhance the accuracy and convergence rate of the basic cuckoo search (CS) algorithm, the adaptive parameters adjusting operation is presented to tune the parameters properly. Besides, the exploitation capability of the CS algorithm is enhanced a lot by integrating the orthogonal design strategy. The functionality of the HACS algorithm is tested through the Lorenz system under the noise-free and noise-corrupted conditions, respectively. The numerical results demonstrate that the algorithm can estimate parameters efficiently and accurately, and the capability of noise immunity is also powerful. Compared with the basic CS algorithm, genetic algorithm, and particle swarm optimization algorithm, the HACS algorithm is energy efficient and superior.