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A Multi-Verse Optimizer with Levy Flights for Numerical Optimization and Its Application in Test Scheduling for Network-on-Chip

机译:一种用于数值优化的带有费率飞行的多版本优化器及其在片上网络测试调度中的应用

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摘要

We propose a new meta-heuristic algorithm named Levy flights multi-verse optimizer (LFMVO), which incorporates Levy flights into multi-verse optimizer (MVO) algorithm to solve numerical and engineering optimization problems. The Original MVO easily falls into stagnation when wormholes stochastically re-span a number of universes (solutions) around the best universe achieved over the course of iterations. Since Levy flights are superior in exploring unknown, large-scale search space, they are integrated into the previous best universe to force MVO out of stagnation. We test this method on three sets of 23 well-known benchmark test functions and an NP complete problem of test scheduling for Network-on-Chip (NoC). Experimental results prove that the proposed LFMVO is more competitive than its peers in both the quality of the resulting solutions and convergence speed.
机译:我们提出了一种新的元启发式算法,称为征航多段优化器(LFMVO),该算法将征航乘以多行优化器(MVO)算法,以解决数值和工程优化问题。当虫洞随机地围绕在迭代过程中获得的最佳Universe重新分布多个Universe(解决方案)时,原始MVO容易陷入停滞状态。由于征费飞行在探索未知的大规模搜索空间方面表现优异,因此它们被整合到了以前最好的宇宙中,从而迫使MVO摆脱了停滞状态。我们在23个著名的基准测试功能的三组上测试了该方法,并对片上网络(NoC)的测试调度提出了NP完整问题。实验结果证明,所提出的LFMVO在解决方案的质量和收敛速度上都比同类产品更具竞争力。

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