Search and Optimization by Metaheuristics 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


Search and Optimization by Metaheuristics

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Ke-Lin Du 作者
Birkhäuser
譯者
2016-7-21 出版日期
434 頁數
USD 63.00 價格
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9783319411927 圖書編碼

Search and Optimization by Metaheuristics 在線電子書 圖書標籤: 啓發式算法   


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Search and Optimization by Metaheuristics 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024



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Search and Optimization by Metaheuristics 在線電子書 著者簡介

Ke-Lin Du, PhD, is Affiliate Associate Professor at Concordia University, Montreal, Quebec, Canada, and Founder and CEO of Xonlink Inc, Ningbo, China.

M.N.S. Swamy, PhD, is Research Professor and Tier I Concordia Research Chair in the Department of Electrical and Computer Engineering at Concordia University, Montreal, Quebec, Canada.


Search and Optimization by Metaheuristics 在線電子書 著者簡介


Search and Optimization by Metaheuristics 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Search and Optimization by Metaheuristics 在線電子書 圖書描述

This textbook provides a comprehensive introduction to nature-inspired metaheuristic methods for search and optimization, including the latest trends in evolutionary algorithms and other forms of natural computing. Over 100 different types of these methods are discussed in detail. The authors emphasize non-standard optimization problems and utilize a natural approach to the topic, moving from basic notions to more complex ones.

An introductory chapter covers the necessary biological and mathematical backgrounds for understanding the main material. Subsequent chapters then explore almost all of the major metaheuristics for search and optimization created based on natural phenomena, including simulated annealing, recurrent neural networks, genetic algorithms and genetic programming, differential evolution, memetic algorithms, particle swarm optimization, artificial immune systems, ant colony optimization, tabu search and scatter search, bee and bacteria foraging algorithms, harmony search, biomolecular computing, quantum computing, and many others. General topics on dynamic, multimodal, constrained, and multiobjective optimizations are also described. Each chapter includes detailed flowcharts that illustrate specific algorithms and exercises that reinforce important topics. Introduced in the appendix are some benchmarks for the evaluation of metaheuristics.

Search and Optimization by Metaheuristics is intended primarily as a textbook for graduate and advanced undergraduate students specializing in engineering and computer science. It will also serve as a valuable resource for scientists and researchers working in these areas, as well as those who are interested in search and optimization methods.

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