Big data analytics often involves complex decision-making processes that require finding efficient cost-performance tradeoffs. Evolutionary algorithms (EAs) have proven to be effective in solving multi-objective optimization problems by exploring the Pareto front, which represents the optimal tradeoffs between conflicting objectives. In this paper, we propose an evolutionary algorithm-based approach for Pareto front exploration in big data analytics. Our approach employs a novel fitness function that incorporates both cost and performance metrics, allowing the algorithm to simultaneously optimize for both objectives. We introduce several mutation and crossover operators tailored for big data analytics, ensuring effective exploration of the solution space. To validate the effectiveness of our approach, we conduct experiments using real-world big data analytics scenarios. The results demonstrate that our evolutionary algorithm-based approach successfully explores the Pareto front, enabling decision-makers to identify optimal cost-performance tradeoffs in big data analytics.
Deepak Gupta1, Deshdeepak Shrivastava2, Anand Kumar Pandey3, Rashmi Pandey4, Gaurav Dubey5
Institute of Technology and Management Gwalior, India1,2,4,5, ITM University,India3
Big Data Analytics, Evolutionary Algorithms, Multi-Objective Optimization, Pareto Front, Cost-Performance Tradeoffs
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 13 , Issue: 4 , Pages: 3021 - 3026 )
Date of Publication :
July 2023
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634
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