Hybrid Four Vector Intelligent Metaheuristic with Differential Evolution for Structural Single-Objective Engineering Optimization

Complex and nonlinear optimization challenges pose significant difficulties for traditional optimizers, which often struggle to consistently locate the global optimum within intricate problem spaces. To address these challenges, the development of hybrid methodologies is essential for solving comple...

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Published inAlgorithms Vol. 17; no. 9; p. 417
Main Authors Fakhouri, Hussam N., Al-Shamayleh, Ahmad Sami, Ishtaiwi, Abdelraouf, Makhadmeh, Sharif Naser, Fakhouri, Sandi N., Hamad, Faten
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2024
Subjects
Algorithms
Benchmarks
Design engineering
Design optimization
Engineering design
Evolutionary computation
Exploitation
Genetic algorithms
Heuristic methods
Industrial design
intelligent
Mathematical optimization
metaheuristic
Methods
Mutation
optimization
Optimization algorithms
Optimization techniques
Statistical analysis
Structural engineering
Task complexity
Jordan
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Summary:Complex and nonlinear optimization challenges pose significant difficulties for traditional optimizers, which often struggle to consistently locate the global optimum within intricate problem spaces. To address these challenges, the development of hybrid methodologies is essential for solving complex, real-world, and engineering design problems. This paper introduces FVIMDE, a novel hybrid optimization algorithm that synergizes the Four Vector Intelligent Metaheuristic (FVIM) with Differential Evolution (DE). The FVIMDE algorithm is rigorously tested and evaluated across two well-known benchmark suites (i.e., CEC2017, CEC2022) and an additional set of 50 challenging benchmark functions. Comprehensive statistical analyses, including mean, standard deviation, and the Wilcoxon rank-sum test, are conducted to assess its performance. Moreover, FVIMDE is benchmarked against state-of-the-art optimizers, revealing its superior adaptability and robustness. The algorithm is also applied to solve five structural engineering challenges. The results highlight FVIMDE’s ability to outperform existing techniques across a diverse range of optimization problems, confirming its potential as a powerful tool for complex optimization tasks.
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ISSN:1999-4893
1999-4893
DOI:10.3390/a17090417