BibTeX
@MISC{Dusan_geneticalgorithm,
author = {Jozef Kratica Dusan},
title = {Genetic Algorithm for Designing a Spread-Spectrum Radar Polyphase Code},
year = {}
}
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Abstract
A genetic algorithm (GA) is proposed for solving one continuous min-max global optimization problem arising from the synthesis of radar polyphase codes. GA implementation is successfully applied to this problem and it solves instances with n up to 20. Computational results are compared to other algorithms (implicit enumeration technique, Monte Carlo method and tabu search). In some cases, GA outperforms all of them. 1. INTRODUCTION Problems of optimal design are important part of the global optimization. In [1] is proposed one such engineering problem: spread-spectrum radar polyphase code design (SSRPCD). 1.1. Problem Definition The SSRPCD problem can be formulated as follows: global min f(x) (1) x X f(x) = max { | j 1 (x)|, | j 2 (x)|, ..., | j m (x)| } X = {(x 1 , x 2 , ... , x n ) R n | 0 x j 2p, j=1, 2, ..., n} where m = 2n-1 and j 2i-1 (x) = =+--= n i j j j i k k x 1 1 2 cos , i=1, 2, ..., n j 2i (x) = 0.5 + + =+-= n i j j j i k k x 112 cos , i=1, ...
Keyphrases
genetic algorithm spread-spectrum radar polyphase code monte carlo method radar polyphase code engineering problem implicit enumeration technique computational result spread-spectrum radar polyphase code design global min continuous min-max global optimization problem problem definition ga implementation introduction problem global optimization optimal design important part ssrpcd problem
