Download An Introduction to Harmony Search Optimization Method by Xiaolei Wang, Xiao-Zhi Gao, Kai Zenger PDF

By Xiaolei Wang, Xiao-Zhi Gao, Kai Zenger

This short presents a close creation, dialogue and bibliographic overview of the nature1-inspired optimization set of rules known as concord seek. It makes use of a great number of simulation effects to illustrate some great benefits of concord seek and its variations and in addition their drawbacks. The authors convey how weaknesses might be amended by means of hybridization with different optimization equipment. The concord seek procedure with functions may be of price to researchers in computational intelligence in demonstrating the cutting-edge of analysis on an set of rules of present curiosity. It additionally is helping researchers and practitioners of electric and laptop engineering extra quite often in acquainting themselves with this system of vector-based optimization.

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Appl. Math. Comput. 198(2), 643–656 (2008) 9. F. Amini, P. Ghaderi, Hybridization of harmony search and ant colony optimization for optimal locating of structural dampers. Appl. Soft Comput. 13(5), 2272–2280 (2013) 10. K. Pan, L. Wang, L. Gao, A chaotic harmony search algorithm for the flow shop scheduling problem with limited buffers. Soft Comput. 11(8), 5270–5280 (2011) 11. J. B. Huang, F. , A heuristic particle swarm optimizer for optimization of pin connected structures. Comput. Struct. 85(7–8), 340–349 (2007) 12.

E. Yazdi, V. T. Haghighat, A new biped locomotion involving arms swing based on neural network with harmony search optimizer. in IEEE International Conference on Automation and Logistics, Chongqing, China, 15–16 June 2011 17. Z. Gao, X. J. Ovaska, A modified harmony search method in constrained optimization. Int. J. Innov. Comput. Inf. Control 6(9), 4235–4247 (2009) 18. Z. Gao, X. J. Ovaska, Harmony search methods for multi-modal and constrained optimization, in Music-Inspired Harmony Search Algorithm, ed.

12 Convergence procedures of CSA, HS, and hybrid HS algorithm in optimization of Sugeno fuzzy classification system for Fisher iris data classification (N = 10). Dotted line CSA, dash line HS method, solid line hybrid HS algorithm In the simulations, N instances from each iris species are randomly selected as the training data, and the remaining instances (T) are regarded as the test data. All the input variables are normalized within the range of [0, 1]. 8, the number of the HM members is five, and the maximum number of the antibody clones is four.

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