Evolutionary Algorithm-based Pareto Front Exploration for Efficient Cost-performance Tradeoffs in Big Data Analytics
Year: 2023
Type: article
Abstract: 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.
Source: ICTACT Journal on Soft Computing
Authors: Deepak Gupta, Deshdeepak Shrivastava, Anand Kumar Pandey, Rashmi Pandey, Gaurav Dubey
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4403768628