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A Parallel Shuffled Frog Leaping Algorithm Based on Stem Regions Combinatorial Optimization for RNA Secondary Structure Prediction

机译:基于茎区的平行洗机青蛙跨越算法,用于RNA二级结构预测的组合优化

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RNA Secondary Structure Prediction is an important part of the biological computing. RNA secondary structure prediction algorithms tend to have higher time and space complexity. Some swarm intelligence algorithms can also be applied to RNA secondary structure prediction on the basis of stem regions combinatorial optimization algorithm, such as genetic algorithm (GA), particle swarm optimization algorithm (PSO) and shuffled frog leaping algorithm (SFLA). And these algorithms achieved good effects. According to shuffled frog leaping algorithm in the application of RNA secondary structure prediction, this paper presents a parallel discrete shuffled frog leaping algorithm (parallel-DSFLA). This parallel algorithm can run on a distributed cluster system using the MPI programming mode. The experimental results show that the parallel-DSFLA got better speed-up ratio, can improve the RNA secondary structure prediction efficiency and save time.
机译:RNA二级结构预测是生物学计算的重要组成部分。 RNA二级结构预测算法往往具有更高的时间和空间复杂性。一些群智能算法也可以基于茎区组合优化算法应用于RNA二级结构预测,例如遗传算法(GA),粒子群优化算法(PSO)和随机跨越跨越算法(SFLA)。这些算法取得了良好的效果。根据随机的青蛙跳跃算法在应用RNA二级结构预测时,本文介绍了一个平行离散的混合青蛙跳跃算法(并行DSFLA)。该并行算法可以使用MPI编程模式在分布式集群系统上运行。实验结果表明,平行DSFLA具有更好的加速比,可以提高RNA二级结构预测效率和节省时间。

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