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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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 As aforementioned, the original HS has its own inherent drawbacks (slow convergence and outdated information).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