Abstract
With the development of the optimization theory, some new intelligent algorithms have been rapidly developed and widely used, and these algorithms have become new methods to solve the traditional system identification problems, such as genetic algorithm, ant colony algorithm, particle swarm optimization algorithm, differential evolution algorithm. These optimization algorithms simulate natural phenomena and processes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
J. Kennedy, R. Eberhart, Particle swarm optimization. IEEE Int. Conf. Neural Netw. 4, 1942–1948 (1995)
R. Storn, K. Price, Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11, 341–359 (1997)
J.J. Hopfield, D.W. Tank, Neural computation of decision in optimization problems. Biol. Cybernrtics 52, 141–152 (1985)
S.Y. Sun, J.L. Zheng, A modified algorithm and theoretical analysis for hopfield neural solving TSP. Acta Electronica Sinca 23(1), 73–78 (1995). (in Chinese)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2018 Tsinghua University Press, Beijing and Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Liu, J. (2018). Intelligent Search Algorithm Design. In: Intelligent Control Design and MATLAB Simulation. Springer, Singapore. https://doi.org/10.1007/978-981-10-5263-7_11
Download citation
DOI: https://doi.org/10.1007/978-981-10-5263-7_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-5262-0
Online ISBN: 978-981-10-5263-7
eBook Packages: EngineeringEngineering (R0)