Abstract
Inspiration drawn from nature and modeling of natural processes are the two common characteristics existing in most NIC algorithms. These methodologies, therefore, share many similarities, e.g., adaptation, learning, and evolution, and have a general flowchart including candidate initialization, operation, and renewal. On the other hand, mimicking various natural phenomena leads to their different generation, evaluation, selection, and update mechanisms, which may result in individual inherent distinctive properties, advantages, as well as drawbacks in the performances of dealing with different optimization problems. For example, the CSA on the basis of modeling the clonal selection principle of the artificial immune system performs well in the local search but suffers from a long convergence time. This chapter compares three typical evolutionary optimization methods, GA, CSA, and HS, with regard to their structures and performances using illustrative examples.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
J.H. Holland, Adaptation in natural and artificial systems (University of Michigan Press, Ann Arbor, 1975)
K.F. Man, K.S. Tang, S. Kwong, Genetic algorithms: Concepts and applications. IEEE Trans. Ind. Electron. 43(5), 519–534 (1996)
K.S. Tang, K.F. Man, S. Kwong et al., Genetic algorithms and their applications. IEEE Signal Process 6, 22–37 (1996)
X.Z. Gao, S.J. Ovaska, Genetic algorithm training of Elman neural network in motor fault detection. Neural Comput. Appl. 11(1), 37–44 (2002)
X. Wang, X.Z. Gao, S.J Ovaska, Artificial immune optimization methods and applications-a survey. in IEEE International Conference on Systems, Man, and Cybernetics, The Hague, The Netherlands, 10–13 Oct 2004
J. Timmis, P. Andrews, N. Owens et al., An interdisciplinary perspective on artificial immune systems. Evolut. Intell. 1(1), 2–26 (2008)
L.N. Castro, F.J. Zuben, Learning and optimization using the clonal selection principle. IEEE Trans. Evolut. Comput. 6(3), 239–251 (2002)
X. Wang, Clonal selection algorithm in power filter optimization. in IEEE Mid-Summer Workshop on Soft Computing in Industrial Applications, Espoo, Finland, 28–30 June 2005
D. Dasgupta, Advances in artificial immune systems. IEEE Comput. Intell. Mag. 1(4), 40–49 (2006)
X. Wang, X.Z. Gao, S.J. Ovaska, A novel particle swarm-based method for nonlinear function optimization. Int. J. Comput. Intell. Res. 4(3), 281–289 (2008)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2015 The Author(s)
About this chapter
Cite this chapter
Wang, X., Gao, XZ., Zenger, K. (2015). The Harmony Search in Context with Other Nature Inspired Computational Algorithms. In: An Introduction to Harmony Search Optimization Method. SpringerBriefs in Applied Sciences and Technology(). Springer, Cham. https://doi.org/10.1007/978-3-319-08356-8_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-08356-8_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08355-1
Online ISBN: 978-3-319-08356-8
eBook Packages: EngineeringEngineering (R0)