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Open Access Journal Article

Hybrid Evolutionary Algorithms for Solving Large-Scale Optimization Problems

by Michael Thomas 1,*
1
Michael Thomas
*
Author to whom correspondence should be addressed.
TASC  2022 4(1):28; https://doi.org/10.69610/j.tasc.20220416
Received: 17 February 2022 / Accepted: 24 March 2022 / Published Online: 16 April 2022
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Abstract

Hybrid evolutionary algorithms (HEAs) have emerged as a powerful approach for addressing large-scale optimization problems. This paper presents a comprehensive analysis of HEAs, focusing on their design, implementation, and application to complex optimization scenarios. The paper begins with an overview of evolutionary algorithms (EAs), highlighting their key principles and strategies. It then delves into the concept of hybridization, discussing the integration of multiple EA components to enhance the search performance. The design of HEAs for large-scale problems is examined, emphasizing the challenges posed by the high dimensionality, nonlinearity, and computational complexity of these problems. Several hybridization techniques are introduced, including the combination of different EA operators, the integration of local search methods, and the use of adaptive parameter tuning. The paper also explores the effectiveness of HEAs in solving real-world problems, such as engineering design, logistics, and data mining. Finally, future directions in the development and application of HEAs are discussed, highlighting the potential for further advancements in the field.

Keywords: Hybrid Evolutionary Algorithms, Large-scale Optimization, Evolutionary Computation, Hybridization Techniques, Real-world Applications.;
Copyright: © 2022 by Thomas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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ACS Style
Thomas, M. Hybrid Evolutionary Algorithms for Solving Large-Scale Optimization Problems. Transactions on Applied Soft Computing, 2022, 4, 28. https://doi.org/10.69610/j.tasc.20220416
AMA Style
Thomas M. Hybrid Evolutionary Algorithms for Solving Large-Scale Optimization Problems. Transactions on Applied Soft Computing; 2022, 4(1):28. https://doi.org/10.69610/j.tasc.20220416
Chicago/Turabian Style
Thomas, Michael 2022. "Hybrid Evolutionary Algorithms for Solving Large-Scale Optimization Problems" Transactions on Applied Soft Computing 4, no.1:28. https://doi.org/10.69610/j.tasc.20220416
APA style
Thomas, M. (2022). Hybrid Evolutionary Algorithms for Solving Large-Scale Optimization Problems. Transactions on Applied Soft Computing, 4(1), 28. https://doi.org/10.69610/j.tasc.20220416

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