Swarm Intelligence: Principles, Advances, and Applications

Hassanien, Aboul Ella, Emary, Eid

Swarm Intelligence: Principles, Advances, and Applications

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商品描述

Swarm Intelligence: Principles, Advances, and Applications delivers in-depth coverage of bat, artificial fish swarm, firefly, cuckoo search, flower pollination, artificial bee colony, wolf search, and gray wolf optimization algorithms. The book begins with a brief introduction to mathematical optimization, addressing basic concepts related to swarm intelligence, such as randomness, random walks, and chaos theory. The text then:

 

 






     
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  • Describes the various swarm intelligence optimization methods, standardizing the variants, hybridizations, and algorithms whenever possible





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  • Discusses variants that focus more on binary, discrete, constrained, adaptive, and chaotic versions of the swarm optimizers





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  • Depicts real-world applications of the individual optimizers, emphasizing variable selection and fitness function design





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  • Details the similarities, differences, weaknesses, and strengths of each swarm optimization method





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  • Draws parallels between the operators and searching manners of the different algorithms

 

 

 

 

Swarm Intelligence: Principles, Advances, and Applications presents a comprehensive treatment of modern swarm intelligence optimization methods, complete with illustrative examples and an extendable MATLAB(R) package for feature selection in wrapper mode applied on different data sets with benchmarking using different evaluation criteria. The book provides beginners with a solid foundation of swarm intelligence fundamentals, and offers experts valuable insight into new directions and hybridizations.

 

 

商品描述(中文翻譯)

《群體智能：原理、進展與應用》深入探討了蝙蝠、人工魚群、螢火蟲、布穀鳥搜尋、花粉傳播、人工蜜蜂群、狼搜尋和灰狼優化算法。本書首先簡要介紹了數學優化，涵蓋了與群體智能相關的基本概念，如隨機性、隨機漫步和混沌理論。接著，本書：

- 詳細描述了各種群體智能優化方法，並在可能的情況下對變體、混合和算法進行標準化。
- 討論了更多關注二進制、離散、受限、適應性和混沌版本的群體優化器的變體。
- 描繪了個別優化器的實際應用，強調變量選擇和適應函數設計。
- 詳細介紹了每種群體優化方法的相似性、差異性、弱點和優勢。
- 將不同算法的運算子和搜索方式進行對比。

《群體智能：原理、進展與應用》提供了現代群體智能優化方法的全面介紹，並附有實例和可擴展的MATLAB套件，用於在不同數據集上應用特徵選擇的包裝模式，並使用不同的評估標準進行基準測試。本書為初學者提供了堅實的群體智能基礎，並為專家提供了有關新方向和混合方法的寶貴見解。

作者簡介

Aboul Ella Hassanien is a full professor in the Information Technology Department of the Faculty of Computers and Information at Cairo University, Giza, Egypt. Widely published and highly decorated, he is the founder and chair of the Scientific Research Group in Egypt, has established the Egyptian Rough Sets Society, and is chairing the Egyptian International Rough Set Society Chapter. He has served as a general chair, co-chair, program chair, and program committee member of various international conferences, and as a reviewer and guest editor for numerous international journals. His research interests include computational intelligence, network security, animal identification, and more.

 

 

Eid Emary is currently a lecturer in the Information Technology Department of the Faculty of Computers and Information at Cairo University, Giza, Egypt. He has authored or co-authored more than 15 research publications in peer-reviewed journals, book chapters, and conference proceedings. He has served as a technical program committee member of various international conferences, and as a reviewer for numerous international journals. His research interests are in the areas of computer vision, mathematical optimization, pattern recognition, video and image processing, machine learning, data mining, and biometrics.

 

 

作者簡介(中文翻譯)

Aboul Ella Hassanien是埃及吉薩開羅大學計算機與資訊學院資訊技術系的全職教授。他廣泛發表並獲得高度肯定，是埃及科學研究團體的創始人和主席，建立了埃及粗糙集學會，並擔任埃及國際粗糙集學會分會主席。他曾擔任多個國際會議的總召集人、聯合主席、程序主席和程序委員會成員，並擔任多個國際期刊的評審和客座編輯。他的研究興趣包括計算智能、網絡安全、動物識別等。

Eid Emary目前是埃及吉薩開羅大學計算機與資訊學院資訊技術系的講師。他在同行評審的期刊、專書章節和會議論文集上發表或合著了15多篇研究論文。他曾擔任多個國際會議的技術程序委員會成員，並擔任多個國際期刊的評審。他的研究興趣涵蓋計算機視覺、數學優化、模式識別、視頻和圖像處理、機器學習、數據挖掘和生物特徵等領域。

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