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Titlebook: Modern Music-Inspired Optimization Algorithms for Electric Power Systems; Modeling, Analysis a Mohammad Kiani-Moghaddam,Mojtaba Shivaie,Phi

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发表于 2025-3-21 16:05:35 | 显示全部楼层 |阅读模式
书目名称Modern Music-Inspired Optimization Algorithms for Electric Power Systems
副标题Modeling, Analysis a
编辑Mohammad Kiani-Moghaddam,Mojtaba Shivaie,Philip D.
视频video
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
丛书名称Power Systems
图书封面Titlebook: Modern Music-Inspired Optimization Algorithms for Electric Power Systems; Modeling, Analysis a Mohammad Kiani-Moghaddam,Mojtaba Shivaie,Phi
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
关键词Melody search algorithm; Powell heuristic method; Power system operation; Power system planning; Power q
版次1
doihttps://doi.org/10.1007/978-3-030-12044-3
isbn_softcover978-3-030-12046-7
isbn_ebook978-3-030-12044-3Series ISSN 1612-1287 Series E-ISSN 1860-4676
issn_series 1612-1287
copyrightSpringer Nature Switzerland AG 2019
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发表于 2025-3-21 22:15:26 | 显示全部楼层
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Book 2019teger 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
发表于 2025-3-22 06:23:19 | 显示全部楼层
Mohammad Kiani-Moghaddam,Mojtaba Shivaie,Philip D.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
发表于 2025-3-22 11:53:13 | 显示全部楼层
978-3-030-12046-7Springer Nature Switzerland AG 2019
发表于 2025-3-22 15:27:44 | 显示全部楼层
Modern Music-Inspired Optimization Algorithms for Electric Power Systems978-3-030-12044-3Series ISSN 1612-1287 Series E-ISSN 1860-4676
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Power Systemshttp://image.papertrans.cn/m/image/637296.jpg
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Book 2019proving 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
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