Evolutionary Large-Scale Multi-Objective Optimization and Applications
Zhang, Xingyi, Cheng, Ran, Tian, Ye
- 出版商: Wiley
- 出版日期: 2024-08-06
- 售價: $4,240
- 貴賓價: 9.5 折 $4,028
- 語言: 英文
- 頁數: 352
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 1394178417
- ISBN-13: 9781394178414
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商品描述
Tackle the most challenging problems in science and engineering with these cutting-edge algorithms
Multi-objective optimization problems (MOPs) are those in which more than one objective needs to be optimized simultaneously. As a ubiquitous component of research and engineering projects, these problems are notoriously challenging. In recent years, evolutionary algorithms (EAs) have shown significant promise in their ability to solve MOPs, but challenges remain at the level of large-scale multi-objective optimization problems (LSMOPs), where the number of variables increases and the optimized solution is correspondingly harder to reach.
Evolutionary Large-Scale Multi-Objective Optimization and Applications constitutes a systematic overview of EAs and their capacity to tackle LSMOPs. It offers an introduction to both the problem class and the algorithms before delving into some of the cutting-edge algorithms which have been specifically adapted to solving LSMOPs. Deeply engaged with specific applications and alert to the latest developments in the field, it#s a must-read for students and researchers facing these famously complex but crucial optimization problems.
The book#s readers will also find:
- Analysis of multi-optimization problems in fields such as machine learning, network science, vehicle routing, and more
- Discussion of benchmark problems and performance indicators for LSMOPs
- Presentation of a new taxonomy of algorithms in the field
Evolutionary Large-Scale Multi-Objective Optimization and Applications is ideal for advanced students, researchers, and scientists and engineers facing complex optimization problems.
商品描述(中文翻譯)
解決科學和工程中最具挑戰性的問題，運用這些尖端演算法
多目標優化問題（MOPs）是指需要同時優化多個目標的問題。作為研究和工程專案中普遍存在的組成部分，這些問題以其挑戰性而聞名。近年來，進化演算法（EAs）在解決 MOPs 的能力上顯示出顯著的潛力，但在大規模多目標優化問題（LSMOPs）層面上仍然存在挑戰，因為變數的數量增加，優化解的獲得相應變得更加困難。
《進化大規模多目標優化及其應用》對 EAs 及其解決 LSMOPs 的能力進行了系統性的概述。它提供了對問題類別和演算法的介紹，然後深入探討一些專門調整以解決 LSMOPs 的尖端演算法。該書深入關注特定應用，並對該領域的最新發展保持警覺，對於面對這些著名的複雜但關鍵的優化問題的學生和研究人員來說，這是一本必讀之作。
本書的讀者還將發現：
- 在機器學習、網路科學、車輛路由等領域的多重優化問題分析
- LSMOPs 的基準問題和性能指標的討論
- 該領域中新演算法分類法的介紹
《進化大規模多目標優化及其應用》非常適合面對複雜優化問題的高級學生、研究人員以及科學家和工程師。
作者簡介
Xingyi Zhang, PhD, is a Professor in the School of Computer Science and Technology at Anhui University, Hefei, China. He serves as an Associate Editor of the IEEE Transactions on Evolutionary Computation, and a member of the editorial board for Complex and Intelligent Systems.
Ran Cheng, PhD, is an Associate Professor in the Department of Computer Science and Engineering at the Southern University of Science and Technology, China. He is an Associate Editor for the IEEE Transactions on Evolutionary Computation, IEEE Transactions on Artificial Intelligence, IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Transactions on Cognitive and Developmental Systems, and ACM Transactions on Evolutionary Learning and Optimization.
Ye Tian, PhD, is an Associate Professor in School of Computer Science and Technology at Anhui University, Hefei, China. He also serves as an Associate Editor of the IEEE Transactions on Evolutionary Computation.
Yaochu Jin, PhD, is a Chair Professor of Artificial Intelligence, Head of the Trustworthy and General Artificial Intelligence Laboratory, Westlake University, China. He was an Alexander von Humboldt Professor of Artificial Intelligence at the Bielefeld University, Germany, and Distinguished Chair in Computational Intelligence at the University of Surrey, United Kingdom.
作者簡介(中文翻譯)
Xingyi Zhang, PhD，是中國合肥安徽大學計算機科學與技術學院的教授。他擔任IEEE《進化計算學報》的副編輯，並且是《複雜與智能系統》期刊的編輯委員會成員。
Ran Cheng, PhD，是中國南方科技大學計算機科學與工程系的副教授。他擔任IEEE《進化計算學報》、《IEEE人工智慧學報》、《IEEE計算智能新興主題學報》、《IEEE認知與發展系統學報》以及《ACM進化學習與優化學報》的副編輯。
Ye Tian, PhD，是中國合肥安徽大學計算機科學與技術學院的副教授。他同時擔任IEEE《進化計算學報》的副編輯。
Yaochu Jin, PhD，是中國西湖大學人工智慧講座教授，並擔任可信與通用人工智慧實驗室的負責人。他曾擔任德國比勒費爾德大學的亞歷山大·馮·洪堡人工智慧教授，以及英國薩里大學計算智能的傑出講座教授。
