Task Allocation in Torus Mesh Networks Using Differential Evolution

Adam Włodarczyk, I. Pozniak-Koszalka, L. Koszalka, A. Kasprzak, D. Zydek
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Abstract

In the paper, the problem of efficient task allocation in torus mesh network is considered. The authors tested the implemented metaheuristic algorithm which is based on Differential Evaluation method. The focus is taken on tuning the algorithm, i.e., choosing the best parameters of mutation scheme. The research was made using the new designed and implemented experimentation system. Ten mutation schemes were taken into consideration. These schemes and a random algorithm as the reference were compared. The results of experiments showed that using one of mutation schemes called DE/rand/1 can ensure the greatest profit.
基于差分进化的环面网格网络任务分配
本文研究环面网格网络中任务的有效分配问题。作者对所实现的基于差分评价法的元启发式算法进行了测试。重点是对算法进行调优,即选择最优的突变方案参数。利用新设计和实现的实验系统进行了研究。考虑了10种突变方案。并将这些方案与随机算法作为参考进行了比较。实验结果表明,采用一种名为DE/rand/1的突变方案可以保证最大的利润。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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