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Titlebook: Metaheuristic Computation: A Performance Perspective; Erik Cuevas,Primitivo Diaz,Octavio Camarena Book 2021 Springer Nature Switzerland AG

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發(fā)表于 2025-3-21 19:59:11 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Metaheuristic Computation: A Performance Perspective
編輯Erik Cuevas,Primitivo Diaz,Octavio Camarena
視頻videohttp://file.papertrans.cn/632/631348/631348.mp4
概述Presents the performance comparison of various metaheuristic techniques when they face complex optimization problems.Particularly focuses on recently developed algorithms.This book is designed so that
叢書名稱Intelligent Systems Reference Library
圖書封面Titlebook: Metaheuristic Computation: A Performance Perspective;  Erik Cuevas,Primitivo Diaz,Octavio Camarena Book 2021 Springer Nature Switzerland AG
描述.This book is primarily intended for undergraduate and postgraduate students of Science, Electrical Engineering, or Computational Mathematics. Metaheuristic search methods are so numerous and varied in terms of design and potential applications; however, for such an abundant family of optimization techniques, there seems to be a question which needs to be answered: Which part of the design in a metaheuristic algorithm contributes more to its better performance? Several works that compare the performance among metaheuristic approaches have been reported in the literature. Nevertheless, they suffer from one of the following limitations: (A)Their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments.? (B) Their conclusions consider only the comparison of their final results which cannot evaluate the nature of a good or bad balance between exploration and exploitation. The objective of this book is to compare the performance of various metaheuristic techniques when they are faced with complex optimization pro
出版日期Book 2021
關(guān)鍵詞Swarm Intelligence; Metaheuristics; Evolutionary computation; Swarm Methods; Metaheuristic Methods
版次1
doihttps://doi.org/10.1007/978-3-030-58100-8
isbn_softcover978-3-030-58102-2
isbn_ebook978-3-030-58100-8Series ISSN 1868-4394 Series E-ISSN 1868-4408
issn_series 1868-4394
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

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https://doi.org/10.1007/978-3-030-58100-8Swarm Intelligence; Metaheuristics; Evolutionary computation; Swarm Methods; Metaheuristic Methods
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Introductory Concepts of Metaheuristic Computation,these approaches. An important propose of this chapter is also to recognize the importance of metaheuristic methods to solve optimization problems in the cases in which traditional techniques are not suitable.
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An Enhanced Swarm Method Based on the Locust Search Algorithm,PSO), Artificial Bee Colony (ABC), Bat Algorithm (BA), Differential Evolution (DE), Harmony Search (HS) and the original Locust Search (LS). Our experimental results show LS-II to be superior to all other compared methods in terms of solution quality and as such proves to be an excellent alternative
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