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Titlebook: An Introduction to Harmony Search Optimization Method; Xiaolei Wang,Xiao-Zhi Gao,Kai Zenger Book 2015 The Editor(s) (if applicable) and Th

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期刊全称An Introduction to Harmony Search Optimization Method
影响因子2023Xiaolei Wang,Xiao-Zhi Gao,Kai Zenger
视频video
发行地址The latest research on emerging optimization method "Harmony Search".Reinforces the power of Harmony Search with hybridization with various other optimization methods.Supported by reliable results fro
学科分类SpringerBriefs in Applied Sciences and Technology
图书封面Titlebook: An Introduction to Harmony Search Optimization Method;  Xiaolei Wang,Xiao-Zhi Gao,Kai Zenger Book 2015 The Editor(s) (if applicable) and Th
影响因子This brief provides a detailed introduction, discussion and bibliographic review of the nature1-inspired optimization algorithm called Harmony Search. It uses a large number of simulation results to demonstrate the advantages of Harmony Search and its variants and also their drawbacks. The authors show how weaknesses can be amended by hybridization with other optimization methods. The Harmony Search Method with Applications will be of value to researchers in computational intelligence in demonstrating the state of the art of research on an algorithm of current interest. It also helps researchers and practitioners of electrical and computer engineering more generally in acquainting themselves with this method of vector-based optimization.
Pindex Book 2015
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The Harmony Search in Context with Other Nature Inspired Computational Algorithms,s, 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 mec
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Bedingte vermittelnde Beobachtungend as a optimization process of adjusting the input (pitches) to obtain the optimal output (perfect harmony). Harmony search draws the inspiration from harmony improvisation, and has gained considerable results in the field of optimization, although it is a relatively NIC algorithm. With mimicking th
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Bedingte vermittelnde Beobachtungenerest in handling a large variety of optimization problems. As aforementioned, the original HS has its own inherent drawbacks (slow convergence and outdated information). Hence, a lot of relevant literature proposes many variations of the HS, which can be classified into two major categories: modifi
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