| 書目名稱 | Modern Music-Inspired Optimization Algorithms for Electric Power Systems | | 副標題 | Modeling, Analysis a | | 編輯 | Mohammad Kiani-Moghaddam,Mojtaba Shivaie,Philip D. | | 視頻video | http://file.papertrans.cn/638/637296/637296.mp4 | | 概述 | Provides an understanding of the optimization problems and algorithms, particularly meta-heuristic optimization algorithms, found in fields such as engineering, economics, management, and operations r | | 叢書名稱 | Power Systems | | 圖書封面 |  | | 描述 | In today’s world, with an increase in the breadth and scope of real-world engineering optimization problems as well as with the advent of big data, improving the performance and efficiency of algorithms for solving such problems has become an indispensable need for specialists and researchers. In contrast to conventional books in the field that employ traditional single-stage computational, single-dimensional, and single-homogeneous optimization algorithms, this book addresses multiple newfound architectures for meta-heuristic music-inspired optimization algorithms. These proposed algorithms, with multi-stage computational, multi-dimensional, and multi-inhomogeneous structures, bring about a new direction in the architecture of meta-heuristic algorithms for solving complicated, real-world, large-scale, non-convex, non-smooth engineering optimization problems having a non-linear, mixed-integer nature with big data. The architectures of these new algorithms may also be appropriate for finding an optimal solution or a Pareto-optimal solution set with higher accuracy and speed in comparison to other optimization algorithms, when feasible regions of the solution space and/or dimensions | | 出版日期 | Book 2019 | | 關(guān)鍵詞 | Melody search algorithm; Powell heuristic method; Power system operation; Power system planning; Power q | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-12044-3 | | isbn_softcover | 978-3-030-12046-7 | | isbn_ebook | 978-3-030-12044-3Series ISSN 1612-1287 Series E-ISSN 1860-4676 | | issn_series | 1612-1287 | | copyright | Springer Nature Switzerland AG 2019 |
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