Affiliations: [a] Department of CSE, Jawaharlal Nehru Technological University Hyderabad, Hyderabad, Telangana, India | [b] Department of CSE, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India
Correspondence:
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Corresponding author: B. Srivani, Research Scholar, Department of CSE, Jawaharlal Nehru Technological University Hyderabad, Hyderabad, Telangana, India. E-mail: [email protected].
Abstract: The significance of big data are prone to complication in solving optimization issues. In several scenarios, one requires adapting several contradictory goals and satisfies various criterions. This made the research on multi-objective optimization more vital and has become main topic. This paper presents theoretical analysis and comparative study of top ten optimization algorithms with respect to DMS. The performance analysis and study of optimization algorithms in big data streaming are explicated. Here, the top ten algorithms of optimization based on recency and popularity are considered. In addition, the performance analysis based on Efficiency, Reliability, Quality of solution, and superiority of DMS algorithm over other top 10 algorithms are examined. From analysis, the DMS provides better efficiency as it endeavours less computational effort to generate better solution, due to acquisition of both DA and MS algorithm’s benefits and DMS takes less time to process a task. Moreover, the DMS needs less number of iterations in the process of optimization and helps to stop optimization process in local optimum. In addition, the DMS has better reliability as it poses the potential to handle specific level of performance. In addition, the DMS utilizes heuristic information for attaining high reliability. Moreover, the DMS produced high computation accuracy, which reveals its solution quality. From the analysis, it is noted that DMS attained improved outcomes in terms of efficiency, reliability and solution quality in contrast to other top 10 optimization algorithms.
Keywords: Big data classification, spark model, optimization, big data streaming, big data
