| 書目名稱 | Multi-Objective Optimization | | 副標題 | Evolutionary to Hybr | | 編輯 | Jyotsna K. Mandal,Somnath Mukhopadhyay,Paramartha | | 視頻video | http://file.papertrans.cn/640/639988/639988.mp4 | | 概述 | Includes multi/many-objective optimization using various computation platforms.Explores the efficacy of hybridization approaches like GA-Fuzz, Neuro-Fuzz, Neuro-ACO etc. in solving real-life applicati | | 圖書封面 |  | | 描述 | .This book brings together the latest findings on efficient solutions of multi/many-objective optimization problems from the leading researchers in the field. The focus is on solving real-world optimization problems using strategies ranging from evolutionary to hybrid frameworks, and involving various computation platforms. .The topics covered include solution frameworks using evolutionary to hybrid models in application areas like Analytics, Cancer Research, Traffic Management, Networks and Communications, E-Governance, Quantum Technology, Image Processing, etc. As such, the book offers a valuable resource for all postgraduate students and researchers interested in exploring solution frameworks for multi/many-objective optimization problems.. | | 出版日期 | Book 2018 | | 關(guān)鍵詞 | Optimization; Multi-objective; Science and Engineering Applications; Computational Intelligence; Mathema | | 版次 | 1 | | doi | https://doi.org/10.1007/978-981-13-1471-1 | | isbn_softcover | 978-981-13-4639-2 | | isbn_ebook | 978-981-13-1471-1 | | copyright | Springer Nature Singapore Pte Ltd. 2018 |
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