Genetic Algorithms in Electromagnetics (Hardcover)

Randy L. Haupt, Douglas H. Werner

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商品描述

A thorough and insightful introduction to using genetic algorithms to optimize electromagnetic systems

Genetic Algorithms in Electromagnetics focuses on optimizing the objective function when a computer algorithm, analytical model, or experimental result describes the performance of an electromagnetic system. It offers expert guidance to optimizing electromagnetic systems using genetic algorithms (GA), which have proven to be tenacious in finding optimal results where traditional techniques fail.

Genetic Algorithms in Electromagnetics begins with an introduction to optimization and several commonly used numerical optimization routines, and goes on to feature:

  • Introductions to GA in both binary and continuous variable forms, complete with examples of MATLAB(r) commands
  • Two step-by-step examples of optimizing antenna arrays as well as a comprehensive overview of applications of GA to antenna array design problems
  • Coverage of GA as an adaptive algorithm, including adaptive and smart arrays as well as adaptive reflectors and crossed dipoles
  • Explanations of the optimization of several different wire antennas, starting with the famous "crooked monopole"
  • How to optimize horn, reflector, and microstrip patch antennas, which require significantly more computing power than wire antennas
  • Coverage of GA optimization of scattering, including scattering from frequency selective surfaces and electromagnetic band gap materials
  • Ideas on operator and parameter selection for a GA
  • Detailed explanations of particle swarm optimization and multiple objective optimization
  • An appendix of MATLAB code for experimentation

商品描述(中文翻譯)

《遺傳演算法在電磁系統優化中的深入且有洞察力的介紹》

《遺傳演算法在電磁學中的應用》專注於當電磁系統的性能由電腦演算法、分析模型或實驗結果描述時,如何優化目標函數。本書提供了使用遺傳演算法(GA)優化電磁系統的專業指導,這種方法已被證明在傳統技術失敗的情況下能夠找到最佳結果。

《遺傳演算法在電磁學中的應用》首先介紹了優化和常用的數值優化方法,然後包括以下內容:

- 介紹了二進制和連續變量形式的遺傳演算法,並提供了MATLAB命令的示例。
- 提供了兩個逐步優化天線陣列的示例,以及對遺傳演算法在天線陣列設計問題中的應用的全面概述。
- 詳細介紹了適應性遺傳演算法,包括適應性和智能陣列,以及適應性反射器和交叉雙極子天線。
- 解釋了幾種不同的導線天線的優化方法,從著名的“彎曲單極天線”開始。
- 如何優化喇叭天線、反射器天線和微帶貼片天線,這些天線需要比導線天線更多的計算能力。
- 遺傳演算法在散射問題中的優化,包括頻率選擇性表面和電磁帶隙材料的散射。
- 遺傳演算法中運算子和參數選擇的想法。
- 詳細解釋了粒子群優化和多目標優化。
- 附錄中提供了用於實驗的MATLAB代碼。