Multi scenario chaotic transient search optimization algorithm for global optimization technique
Abstract Recently, chaotic maps (CMs) have been employed in many optimization algorithms as a motivator to find a better solution to non-convex engineering problems since they can avoid local optima and find the near-optimal solution rapidly. In this article, a metaheuristic, physics-based algorithm...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-86757-7 |
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| author | Ibrahim Mohamed Diaaeldin Hany M. Hasanien Mohammed H. Qais Saad Alghuwainem Othman A. M. Omar |
| author_facet | Ibrahim Mohamed Diaaeldin Hany M. Hasanien Mohammed H. Qais Saad Alghuwainem Othman A. M. Omar |
| author_sort | Ibrahim Mohamed Diaaeldin |
| collection | DOAJ |
| description | Abstract Recently, chaotic maps (CMs) have been employed in many optimization algorithms as a motivator to find a better solution to non-convex engineering problems since they can avoid local optima and find the near-optimal solution rapidly. In this article, a metaheuristic, physics-based algorithm called chaotic transient search optimization (CTSO) algorithm is developed to solve 23 benchmark functions, including uni- and multi-modal optimization functions. Nine CMs integrated into the TSO to improve its search capabilities by applying various scenarios for improving the TSO random numbers. Further, the proposed CTSO was compared with the original TSO using the Wilcoxon p-value test, non-parametric sign test, t-test, convergence curves, and elapsed time. Furthermore, the proposed CTSO algorithm has been employed for solving real-life engineering design problems, including coil spring, welded beam, and pressure vessel design, where CTSO performed better than some recent optimization algorithms in finding the best design. |
| format | Article |
| id | doaj-art-faa36b80c46c49879a2123e993e286bb |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-faa36b80c46c49879a2123e993e286bb2025-02-09T12:34:44ZengNature PortfolioScientific Reports2045-23222025-02-0115113310.1038/s41598-025-86757-7Multi scenario chaotic transient search optimization algorithm for global optimization techniqueIbrahim Mohamed Diaaeldin0Hany M. Hasanien1Mohammed H. Qais2Saad Alghuwainem3Othman A. M. Omar4Engineering Physics and Mathematics Department, Faculty of Engineering, Ain Shams UniversityElectric Power and Machines Department, Faculty of Engineering, Ain Shams UniversityInstitute for Energy Systems, School of Engineering, The University of EdinburghElectrical Engineering Department, College of Engineering, King Saud UniversityEngineering Physics and Mathematics Department, Faculty of Engineering, Ain Shams UniversityAbstract Recently, chaotic maps (CMs) have been employed in many optimization algorithms as a motivator to find a better solution to non-convex engineering problems since they can avoid local optima and find the near-optimal solution rapidly. In this article, a metaheuristic, physics-based algorithm called chaotic transient search optimization (CTSO) algorithm is developed to solve 23 benchmark functions, including uni- and multi-modal optimization functions. Nine CMs integrated into the TSO to improve its search capabilities by applying various scenarios for improving the TSO random numbers. Further, the proposed CTSO was compared with the original TSO using the Wilcoxon p-value test, non-parametric sign test, t-test, convergence curves, and elapsed time. Furthermore, the proposed CTSO algorithm has been employed for solving real-life engineering design problems, including coil spring, welded beam, and pressure vessel design, where CTSO performed better than some recent optimization algorithms in finding the best design.https://doi.org/10.1038/s41598-025-86757-7Physics-based optimization algorithmsTransient search optimizationChaotic mapsErgodicityMetaheuristic algorithms |
| spellingShingle | Ibrahim Mohamed Diaaeldin Hany M. Hasanien Mohammed H. Qais Saad Alghuwainem Othman A. M. Omar Multi scenario chaotic transient search optimization algorithm for global optimization technique Scientific Reports Physics-based optimization algorithms Transient search optimization Chaotic maps Ergodicity Metaheuristic algorithms |
| title | Multi scenario chaotic transient search optimization algorithm for global optimization technique |
| title_full | Multi scenario chaotic transient search optimization algorithm for global optimization technique |
| title_fullStr | Multi scenario chaotic transient search optimization algorithm for global optimization technique |
| title_full_unstemmed | Multi scenario chaotic transient search optimization algorithm for global optimization technique |
| title_short | Multi scenario chaotic transient search optimization algorithm for global optimization technique |
| title_sort | multi scenario chaotic transient search optimization algorithm for global optimization technique |
| topic | Physics-based optimization algorithms Transient search optimization Chaotic maps Ergodicity Metaheuristic algorithms |
| url | https://doi.org/10.1038/s41598-025-86757-7 |
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