文章基本信息

标题：Social Spider Cloud Web Algorithm (SSCWA): a new Meta-Heuristic for Avoiding Premature Convergence in Cloud
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作者：Preeti Abrol ; Dr. Savita Gupta ; Karanpreet Kaur 等
期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2015
卷号：3
期号：6
DOI：10.15680/ijircce.2015.0306113
出版社：S&S Publications
摘要：The increase in the complexity of load balancing problems has encouraged researchers to find someeffective solutions. A load balancing method helps to optimally utilize the available resources, therefore reducingresponse time and improvising the resource utilization. Nature inspired Evolutionary Meta-heuristics and Algorithmsfor load balancing perform better as compared to the conventional optimization algorithms. In this paper, a novelswarm intelligence based meta-heuristic named as Social Spider Cloud Web Algorithm (SSCWA) for ResourcePlacement in cloud environment is proposed that helps in reducing the premature convergence and local minimaproblem considerably. The algorithm is inspired from the foraging behavior of social spiders in their colony, whichinteract through the vibrations that propagate over the spider web so as to determine the position of prey.When simulated, SSCWA outperforms in comparison to the Ant Colony Optimization and its other variant algorithmsand demonstrates the stability, effectiveness and efficiency of the proposed method
关键词：Social Spider Cloud Web Algorithm; Load balancing; Swarm Intelligence; Ant Colony Optimization;Evolutionary Computation; meta-heuristic