HWOA‐TTA: A New Hybrid Metaheuristic Algorithm for Global Optimization and Engineering Design Applications

Hybrid metaheuristics is one of the most exciting improvements in optimization and metaheuristic algorithms. A current research topic combines two algorithms to provide a more advanced solution to optimization problems. The present study applies a new approach called HWOA‐TTA which means a hybrid of...

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Published in:International journal of mathematics and mathematical sciences Vol. 2024; no. 1
Main Authors: Najm, Huda Y., Khaleel, Elaf Sulaiman, Hamed, Eman T., Ahmed, Huda I.
Format: Journal Article
Language:English
Published: New York John Wiley & Sons, Inc 2024
Wiley
Subjects:
Algorithms
Benchmarks
Design engineering
Design optimization
Exploitation
Global optimization
Heuristic methods
Mathematical models
Optimization algorithms
Optimization techniques
Whales & whaling
ISSN:0161-1712, 1687-0425
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Abstract Hybrid metaheuristics is one of the most exciting improvements in optimization and metaheuristic algorithms. A current research topic combines two algorithms to provide a more advanced solution to optimization problems. The present study applies a new approach called HWOA‐TTA which means a hybrid of the whale optimizer algorithm (WOA) and tiki‐taka algorithm (TTA). The hybrid WOA‐TTA combines the exploitation phase of WOA with the exploration phase of TTA. Two stages in the hybridized model are suggested. First, the WOA exploitation phase incorporates the TTA mechanism. Second, a new approach is included in the research phase to enhance the result with each iteration to a set of unconstrained benchmark test functions and engineering design applications. To verify the performance of the improved algorithm, thirteen benchmark functions have been used to compare HWOA‐TTA with the classical intelligent population algorithms (PSO, TTA, and WOA). The hybrid algorithm is applied to two well‐known engineering mathematical models. The experiments show that the HWOA‐TTA outperforms other algorithms.
AbstractList Hybrid metaheuristics is one of the most exciting improvements in optimization and metaheuristic algorithms. A current research topic combines two algorithms to provide a more advanced solution to optimization problems. The present study applies a new approach called HWOA-TTA which means a hybrid of the whale optimizer algorithm (WOA) and tiki-taka algorithm (TTA). The hybrid WOA-TTA combines the exploitation phase of WOA with the exploration phase of TTA. Two stages in the hybridized model are suggested. First, the WOA exploitation phase incorporates the TTA mechanism. Second, a new approach is included in the research phase to enhance the result with each iteration to a set of unconstrained benchmark test functions and engineering design applications. To verify the performance of the improved algorithm, thirteen benchmark functions have been used to compare HWOA-TTA with the classical intelligent population algorithms (PSO, TTA, and WOA). The hybrid algorithm is applied to two well-known engineering mathematical models. The experiments show that the HWOA-TTA outperforms other algorithms.
Audience Academic
Author Najm, Huda Y.
Hamed, Eman T.
Ahmed, Huda I.
Khaleel, Elaf Sulaiman
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Copyright COPYRIGHT 2024 John Wiley & Sons, Inc.
Copyright © 2024 Huda Y. Najm et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0
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Benchmarks
Design engineering
Design optimization
Exploitation
Global optimization
Heuristic methods
Mathematical models
Optimization algorithms
Optimization techniques
Whales & whaling
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