By Xiaolei Wang, Xiao-Zhi Gao, Kai Zenger
This short presents a close creation, dialogue and bibliographic evaluate of the nature1-inspired optimization set of rules known as concord seek. It makes use of a good number of simulation effects to illustrate some great benefits of concord seek and its versions and likewise their drawbacks. The authors exhibit how weaknesses should be amended by means of hybridization with different optimization equipment. The concord seek approach with purposes may be of worth to researchers in computational intelligence in demonstrating the state-of-the-art of study on an set of rules of present curiosity. It additionally is helping researchers and practitioners of electric and desktop engineering extra often in acquainting themselves with this technique of vector-based optimization.
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Extra resources for An Introduction to Harmony Search Optimization Method
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.
That is to say, all the members of the HM are regarded as the individual antibodies, and they can evolve in the population of the CSA. For Â Ã Â Ã example, xi1 ; xi2 ; . ; xin is updated to x0 i1 ; x0 i2 ; . ; x0 in so as to gain a better affinity with the antigen after a certain number of the CSA iterations. The CSA-based update of the HM members is indeed embedded into the HS method as a separate fine-tuning approach. 11 illustrates how the CSA is merged with the HS method in our hybrid HS optimization scheme.
14 show the initial and optimized membership functions of Small, Medium, and Large, respectively. , SW, PL, and PW, and assigning two membership functions to each of them. The three new fuzzy classification rules are as follows: 1. IF SW is Large and PL is Small and PW is Small, THEN iris is Setosa. 2. IF SW is Small and PL is Small and PW is Small, THEN iris is Versicolor. 3. IF SW is Large and PL is Large and PW is Large, THEN iris is Virginica. The optimal membership functions acquired by the hybrid HS method are shown in Fig.
An Introduction to Harmony Search Optimization Method by Xiaolei Wang, Xiao-Zhi Gao, Kai Zenger