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Intellectual Technologies on Transport
Journals / Intellectual Technologies on Transport / Issue 1 / Optimizing Industry Trade-Off Problems in Big Data Management Using Evolutionary Algorithms: A Comparative Study
UDC 004.021

OPTIMIZING INDUSTRY TRADE-OFF PROBLEMS IN BIG DATA MANAGEMENT USING EVOLUTIONARY ALGORITHMS: A COMPARATIVE STUDY Optimizing Industry Trade-Off Problems in Big Data Management Using Evolutionary Algorithms: A Comparative Study

Published in Intellectual Technologies on Transport · Issue 1 · Pages 5–11 · Rubric: Artificial intelligence and machine learning
DOI: https://doi.org/10.20295/2413-2527-2024-137-5-11
Received: 10.04.2024 Accepted: 14.04.2024 Published: 14.04.2024 Language of publication: ENG
Authors
AH
Ahmed Abbas
ABSTRACT
This paper proposes a novel approach to solve complex industrial big data management problems using genetic algorithms (GA), particle swarm optimization (PSO), ant algorithms (ACO) and cultural algorithms (CA). The research aims at efficient resource allocation, balancing conflicting objectives such as cost minimization, resource utilization and quality improvement. The proposed approach offers a comprehensive framework that combines the advantages of different optimization techniques, providing decision makers with important insights into optimal big data strategies in their industries. The results of the study show the effectiveness of the hybrid approach in achieving optimal decisions, which improves operational efficiency and strategic decision making in the era of big data.
KEYWORDS
bigdata, ant colony optimization, cultural algorithms, genetic algorithm, particle swarm optimization
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