Initialization and Diversity in Optimization Algorithms
暫譯: 優化演算法中的初始化與多樣性
Oliva, Diego, Cisneros, Marco Antonio Perez, Morales-Castañeda, Bernardo
- 出版商: CRC
- 出版日期: 2026-02-19
- 售價: $7,200
- 貴賓價: 9.8 折 $7,056
- 語言: 英文
- 頁數: 226
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 1032695811
- ISBN-13: 9781032695815
-
相關分類:
Machine Learning
海外代購書籍(需單獨結帳)
商品描述
Designing new algorithms in swarm intelligence is a complex undertaking. Two critical factors have been seen to have a direct correlation with positive results. First is initialization, which serves as the initial step for all swarm intelligence techniques. Candidate solutions are generated to form the initial population, which are subsequently modified during the iterative process. A well-initialized population increases the algorithm's chances of avoiding local optima and finding the global optimum in fewer iterations. Although random distributions are commonly used for initialization, there are various ways to initialize the population elements.
Maintaining diversity among the population elements throughout the iterative process is also essential. This diversity facilitates a more thorough and efficient exploration of the search space. In swarm intelligence algorithms, there are multiple methods to measure diversity, each with its own advantages and disadvantages.
This book presents the theory behind the initialization process and the different mechanisms. Additionally, it includes a comparative study of various diversity indicators. It explores different methodologies to compute its value and explains how it can be incorporated as a mechanism for deciding when to apply operators during the optimization process. Multiple examples are provided in the book using two classical algorithms: Differential Evolution and Particle Swarm Optimization. It includes MATLAB(R) code and offers several exercises that readers can use for experimentation and design purposes.
商品描述(中文翻譯)
設計新的群體智慧演算法是一項複雜的任務。有兩個關鍵因素被認為與正面結果有直接的相關性。首先是初始化，這是所有群體智慧技術的初始步驟。候選解決方案被生成以形成初始族群，這些解決方案在迭代過程中隨後會被修改。良好初始化的族群增加了演算法避免局部最優解並在較少的迭代中找到全局最優解的機會。雖然隨機分佈通常用於初始化，但有多種方法可以初始化族群元素。
在迭代過程中保持族群元素之間的多樣性也是至關重要的。這種多樣性促進了對搜尋空間的更徹底和高效的探索。在群體智慧演算法中，有多種方法可以衡量多樣性，每種方法都有其優缺點。
本書介紹了初始化過程背後的理論及不同的機制。此外，它還包括對各種多樣性指標的比較研究。它探討了計算其值的不同方法論，並解釋了如何將其作為決定在優化過程中何時應用運算子的一種機制。本書提供了多個範例，使用兩個經典演算法：差分演化（Differential Evolution）和粒子群優化（Particle Swarm Optimization）。它包含 MATLAB(R) 代碼，並提供幾個練習，供讀者用於實驗和設計目的。
作者簡介
Mario Alberto Navarro Velázquez obtained a Master of Science in Electronic Engineering and Computer Science in 2019, focusing his research on the design of metaheuristic algorithms and applications in image segmentation. In 2023, he obtained a PhD in Electronic and Computer Engineering at the University Center for Exact Sciences and Engineering (CUCEI) in Guadalajara, Mexico, concentrating on the coevolution of metaheuristic strategies to solve various optimization problems. His most recent recognitions include becoming a part of the National System of Researchers obtaining the distinction as a national researcher level 1 (SNI 1), and membership in the Mexican Academy of Computing (AmexComp). His research interests include artificial intelligence, specifically the design and hybridization of evolutionary algorithms, the development of operators and hyper heuristics to solve high-dimensional problems, and the integration of evolutionary algorithms and machine learning.
Bernardo Morales Castañeda obtained a Bachelor's in Computer Engineering from the University of Guadalajara (UdeG), Mexico in 2016 and a Master's degree in Electronic and Computer Engineering from University of Guadalajara (UDG) in 2018. He obtained a Doctorate degree in Electronics and Computer Science in 2022. Since 2022, Dr. Morales has been serving as a research professor in the Department of Information and Knowledge-based Innovation at the University Center for Exact Sciences and Engineering (CUCEI) of UDG. His research areas include various branches of AI such as artificial neural networks, computer vision, image segmentation, and the development of metaheuristic algorithms. Since 2023, Dr. Morales has been recognized as a member of the National System of Researchers (SNI) with the distinction of National Researcher Level 1.
Itzel Aranguren obtained a degree in Biomedical Engineering from the Universidad de Guadalajara (UDG), Mexico (2016). In 2018, she obtained a Master's of Science in Electronic Engineering and Computing from UDG and successively, and a Doctor in Electronics and Computer Sciences in 2022. Since 2019, Dr. Aranguren has been a research professor in the Department of Computational Sciences of the University Center for Exact Sciences and Engineering (CUCEI) of the UDG. Dr. Aranguren develops her research in medical image enhancement, metaheuristic algorithms, optimization, and vision. She is recognized as a member of the National System of Researchers (SNI), having the distinction of National Researcher Level 1.
Diego Oliva (Senior Member, IEEE) received a B.Eng. in Electronics and Computer Engineering from the Industrial Technical Education Center (CETI) of Guadalajara, Mexico, in 2007, and a M.Sc. in Electronic Engineering and Computer Sciences from the University of Guadalajara, Mexico, in 2010. He obtained a PhD in Informatics in 2015 from the Universidad Complutense de Madrid. Currently, he is a Professor at the University of Guadalajara in Mexico. He has the National Researcher Rank 2 distinction by the Mexican Council of Science and Technology. In 2022, he obtained the distinction of Highly Cited Researcher by Clarivate (WoS). He has been listed in the world's 2% most cited scientists according to Stanford University and Elsevier since 2022. He also serves as editor for journals such as IEEE Access, Engineering Applications of Artificial Intelligence, Swarm and Evolutionary Computation, and Knowledge-Based Systems, among others. His research interests include evolutionary and swarm algorithms, hybridization of evolutionary and swarm algorithms, and computational intelligence.
Marco Perez-Cisneros (Senior Member, IEEE) has a B.Eng. in communications and electronics engineering from the University of Guadalajara, Mexico, a M. Eng. from ITESO University Mexico, and the PhD from The University of Manchester, UK. He is a Professor with the Electro-Photonics Department and has been appointed as the Chancellor of the University Centre of Exact Sciences and Engineering, University of Guadalajara. He is a member of the National Research System in Mexico. Since 2018, he has been a member of the Mexican National Science Academy. He is a Regular Member of the IET, UK. He also serves as an Associate Editor for IEEE Letters.
作者簡介(中文翻譯)
**馬里奧·阿爾貝托·納瓦羅·維拉斯克斯**於2019年獲得電子工程與計算機科學碩士學位，研究重點為元啟發式演算法的設計及其在影像分割中的應用。2023年，他在墨西哥瓜達拉哈拉的精確科學與工程大學中心（CUCEI）獲得電子與計算機工程博士學位，專注於解決各種優化問題的元啟發式策略的共同演化。他最近的榮譽包括成為國家研究系統的一部分，獲得國家研究員1級（SNI 1）的榮譽，以及墨西哥計算機學會（AmexComp）的會員。他的研究興趣包括人工智慧，特別是進化演算法的設計與混合、高維問題的運算子與超啟發式的開發，以及進化演算法與機器學習的整合。
**伯納多·莫拉萊斯·卡斯塔涅達**於2016年在墨西哥瓜達拉哈拉大學（UdeG）獲得計算機工程學士學位，並於2018年獲得電子與計算機工程碩士學位。2022年，他獲得電子與計算機科學博士學位。自2022年以來，莫拉萊斯博士一直擔任瓜達拉哈拉大學（UDG）精確科學與工程大學中心資訊與知識創新系的研究教授。他的研究領域包括人工智慧的各個分支，如人工神經網絡、計算機視覺、影像分割及元啟發式演算法的開發。自2023年以來，莫拉萊斯博士被認可為國家研究系統（SNI）成員，並獲得國家研究員1級的榮譽。
**伊茲爾·阿蘭古倫**於2016年在墨西哥瓜達拉哈拉大學（UDG）獲得生物醫學工程學位。2018年，她獲得電子工程與計算碩士學位，並於2022年獲得電子與計算機科學博士學位。自2019年以來，阿蘭古倫博士一直擔任瓜達拉哈拉大學（UDG）精確科學與工程大學中心計算科學系的研究教授。阿蘭古倫博士的研究集中在醫學影像增強、元啟發式演算法、優化及視覺領域。她被認可為國家研究系統（SNI）成員，並獲得國家研究員1級的榮譽。
**迭戈·奧利瓦**（IEEE資深會員）於2007年在墨西哥瓜達拉哈拉的工業技術教育中心（CETI）獲得電子與計算機工程學士學位，並於2010年在墨西哥瓜達拉哈拉大學獲得電子工程與計算科學碩士學位。他於2015年在馬德里康普頓斯大學獲得資訊學博士學位。目前，他是墨西哥瓜達拉哈拉大學的教授。他獲得墨西哥科學與技術委員會的國家研究員2級榮譽。2022年，他獲得Clarivate（WoS）頒發的高被引研究者榮譽。自2022年以來，他被斯坦福大學和Elsevier列為全球2%最被引用的科學家之一。他還擔任IEEE Access、Engineering Applications of Artificial Intelligence、Swarm and Evolutionary Computation及Knowledge-Based Systems等期刊的編輯。他的研究興趣包括進化與群體演算法、進化與群體演算法的混合以及計算智慧。
**馬可·佩雷斯-西斯內羅斯**（IEEE資深會員）擁有墨西哥瓜達拉哈拉大學的通訊與電子工程學士學位、ITESO大學的碩士學位，以及英國曼徹斯特大學的博士學位。他是電子光子學系的教授，並被任命為瓜達拉哈拉大學精確科學與工程大學中心的校長。他是墨西哥國家研究系統的成員。自2018年以來，他是墨西哥國家科學學院的成員。他也是英國工程技術學會（IET）的正式會員，並擔任IEEE Letters的副編輯。
