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首页> 外文期刊>Journal of Economics and Sustainable Development >Decoder based on Parallel Genetic Algorithm and Multi-objective Optimization for Low Density Parity Check Codes
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Decoder based on Parallel Genetic Algorithm and Multi-objective Optimization for Low Density Parity Check Codes

机译:基于并行遗传算法的解码器和低密度奇偶校验校验代码的多目标优化

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Genetic algorithms are powerful search techniques that are used successfully to solve problems in many different disciplines. This article introduces a new Parallel Genetic Algorithm for decoding LDPC codes (PGAD). The results show that the proposed algorithm gives large gains over the Sum-Product decoder, which proves its efficiency. We also show that the fitness function must be improved by Multi-objective Optimization, for this, we applied the Weighted Sum method to improve PGAD, this new version is called (MOGAD) gives higher performance compared to one. Keywords: Parallel Genetic Algorithms decoder, Sum-Product decoder, Fitness Function, LDPC codes, Error correcting codes, Multi-objective optimization, Weighted sum method.
机译:遗传算法是强大的搜索技术,用于成功用于解决许多不同学科的问题。本文介绍了一种用于解码LDPC代码(PGAD)的新并行遗传算法。结果表明,该算法对总和 - 产品解码器提供了大的收益,证明其效率。我们还表明,必须通过多目标优化来提高健身功能,为此,我们应用了加权和方法来改善PGAD,这个新版本称为(MoGad)与一个相比提供更高的性能。关键词:并行遗传算法解码器,总和 - 产品解码器,健身功能,LDPC代码,纠错码,多目标优化,加权和方法。

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