Genetic Algorithms in Electromagnetics (Hardcover)
暫譯: 電磁學中的遺傳演算法 (精裝版)

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)

徹底且深入的介紹使用遺傳演算法來優化電磁系統

電磁學中的遺傳演算法 專注於當電腦演算法、分析模型或實驗結果描述電磁系統性能時,如何優化目標函數。它提供專業指導,使用遺傳演算法 (GA) 來優化電磁系統,這些演算法在傳統技術失敗的情況下,已證明能夠堅韌地找到最佳結果。

電磁學中的遺傳演算法 開始於對優化的介紹以及幾個常用的數值優化例程,接著涵蓋以下內容:


  • 對二進位和連續變數形式的 GA 進行介紹,並附有 MATLAB(r) 命令的範例

  • 兩個逐步優化天線陣列的範例,以及對 GA 在天線陣列設計問題中的應用的全面概述

  • GA 作為自適應演算法的涵蓋,包括自適應和智能陣列,以及自適應反射器和交叉偶極子

  • 對幾種不同線天線的優化解釋,從著名的「彎曲單極天線」開始

  • 如何優化喇叭天線、反射器和微帶貼片天線,這些天線需要比線天線顯著更多的計算能力

  • GA 優化散射的涵蓋,包括來自頻率選擇表面和電磁帶隙材料的散射

  • 對 GA 的運算子和參數選擇的想法

  • 粒子群優化和多目標優化的詳細解釋

  • 實驗用的 MATLAB 代碼附錄

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