An Efficient Comparison Analysis of Scheduling Algorithms with a base of Genetic algorithm to Optimization technique of Ant colony and Artificial Bee colony

Q4 Materials Science Solid State Technology Pub Date : 2020-02-29 DOI:10.37896/jxu14.4/120
K. Malathi, K. Priyadarsini
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引用次数: 0

Abstract

Day by Day so many Task scheduling algorithms are booming in the Cloud computing field. However they are having their own limitations. Now we are in the state to analyze all the algorithms and find out the better one for our efficient output. We know that Genetic Algorithm is one of the powerful metaheuristic Algorithms in task scheduling. But due to the random selection of parameters the process quality is not high. So we go for the combination of two or more optimization Algorithms counts on less execution time, maximum throughput, less makespan, full resource utilization, better quality of service, maximum energy consumption, Quick response time and less cost. In this paper we can analysis Genetic optimization hybrid ACO Algorithm, and genetic algorithm Hybrid with ABC Algorithm .Finally the analysis is tabulated for finding the better Algorithm.
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基于遗传算法的调度算法与蚁群和人工蜂群优化技术的高效比较分析
在云计算领域,越来越多的任务调度算法正在蓬勃发展。然而,它们也有自己的局限性。现在我们正处于分析所有算法的状态,并找出更好的算法来实现高效输出。我们知道,遗传算法是任务调度中一种强大的元启发式算法。但由于参数的随机选择,工艺质量不高。因此,我们选择两种或两种以上优化算法的组合,这取决于更少的执行时间、最大的吞吐量、更少的完工时间、充分的资源利用率、更好的服务质量、最大的能耗、快速的响应时间和更低的成本。本文对遗传优化混合ACO算法、遗传算法混合ABC算法进行了分析,最后将分析结果制成表格,找出更好的算法。
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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来源期刊
Solid State Technology
Solid State Technology 工程技术-工程:电子与电气
CiteScore
0.30
自引率
0.00%
发文量
0
审稿时长
>12 weeks
期刊介绍: Information not localized
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