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
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Abstract	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.
AbstractList	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.
Author	Turgut, Oguz Emrah Turgut, Mert Sinan
Author_xml	– sequence: 1 givenname: Oguz Emrah surname: Turgut fullname: Turgut, Oguz Emrah organization: Department of Industrial Engineering, Faculty of Engineering and Architecture, Izmir Bakircay University – sequence: 2 givenname: Mert Sinan surname: Turgut fullname: Turgut, Mert Sinan email: sinanturgut@me.com organization: Department of Mechanical Engineering, Faculty of Engineering, Izmir Democracy University
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Keywords	Benchmark functions Runge-Kutta optimization algorithm Dynamic-opposite learning Opposition-based learning Constrained optimization
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Snippet	The Runge-Kutta Optimization (RUNGE) algorithm is a recently proposed metaphor-free metaheuristic optimizer borrowing practical mathematical foundations of the...
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SubjectTerms	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
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Title	Quasi-dynamic opposite learning enhanced Runge-Kutta optimizer for solving complex optimization problems
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