Quasi-dynamic opposite learning enhanced Runge-Kutta optimizer for solving complex optimization problems

The Runge-Kutta Optimization (RUNGE) algorithm is a recently proposed metaphor-free metaheuristic optimizer borrowing practical mathematical foundations of the famous Runge-Kutta differential equation solver. Despite its relatively new emergence, this algorithm has several applications in various br...

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Veröffentlicht in:Evolutionary intelligence Jg. 17; H. 4; S. 2899 - 2962
Hauptverfasser: Turgut, Oguz Emrah, Turgut, Mert Sinan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2024
Springer Nature B.V
Schlagworte:
Algorithms
Applications of Mathematics
Artificial Intelligence
Bioinformatics
Constraints
Control
Design engineering
Differential equations
Engineering
Exploitation
Genetic algorithms
Heuristic
Heuristic methods
Hyperspaces
Learning
Mathematical and Computational Engineering
Mechatronics
Optimization
Optimization algorithms
Optimization techniques
Ordinary differential equations
Physics
Research Paper
Robotics
Runge-Kutta method
Searching
Space exploration
Statistical Physics and Dynamical Systems
Benchmark functions
Runge-Kutta optimization algorithm
Dynamic-opposite learning
Opposition-based learning
Constrained optimization
ISSN:1864-5909, 1864-5917
Online-Zugang:Volltext
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Zusammenfassung:The Runge-Kutta Optimization (RUNGE) algorithm is a recently proposed metaphor-free metaheuristic optimizer borrowing practical mathematical foundations of the famous Runge-Kutta differential equation solver. Despite its relatively new emergence, this algorithm has several applications in various branches of scientific fields. However, there is still much room for improvement as it suffers from premature convergence resulting from inefficient search space exploration. To overcome this algorithmic drawback, this research study proposes a brand-new quasi-dynamic opposition-based learning (QDOPP) mechanism to be implemented in a standard Runge-Kutta optimizer to eliminate the local minimum points over the search space. Enhancing the asymmetric search hyperspace by taking advantage of various positions of the current solution within the domain is the critical novelty to enrich general diversity in the population, significantly improving the algorithm’s overall exploration capability. To validate the effectivity of the proposed RUNGE-QDOPP method, thirty-four multidimensional optimization benchmark problems comprised of unimodal and multimodal test functions with various dimensionalities have been solved, and the corresponding results are compared against the predictions obtained from the other opposition-based learning variants as well as some state-of-art literature optimizers. Furthermore, six constrained engineering design problems with different functional characteristics have been solved, and the respective results are benchmarked against those obtained for the well-known optimizers. Comparison of the solution outcomes with literature optimizers for constrained and unconstrained test problems reveals that the proposed QDOPP has significant advantages over its counterparts regarding solution accuracy and efficiency.
Bibliographie:ObjectType-Article-1
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ISSN:1864-5909
1864-5917
DOI:10.1007/s12065-024-00919-6