Trochoid Search Optimization
Fuente:
arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914801324654592 |
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| author | Layeb, Abdesslem |
| author_facet | Layeb, Abdesslem |
| contents | This paper introduces the Trochoid Search Optimization Algorithm (TSO), a novel metaheuristic leveraging the mathematical properties of trochoid curves. The TSO algorithm employs a unique combination of simultaneous translational and rotational motions inherent in trochoids, fostering a refined equilibrium between explorative and exploitative search capabilities. Notably, TSO consists of two pivotal phases global and local search that collectively contribute to its efficiency and efficacy. Experimental validation demonstrates the TSO algorithm's remarkable performance across various benchmark functions, showcasing its competitive edge in balancing exploration and exploitation within the search space. A distinguishing feature of TSO lies in its simplicity, marked by a minimal requirement for user-defined parameters, making it an accessible yet powerful optimization tool. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_13597 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Trochoid Search Optimization Layeb, Abdesslem Neural and Evolutionary Computing This paper introduces the Trochoid Search Optimization Algorithm (TSO), a novel metaheuristic leveraging the mathematical properties of trochoid curves. The TSO algorithm employs a unique combination of simultaneous translational and rotational motions inherent in trochoids, fostering a refined equilibrium between explorative and exploitative search capabilities. Notably, TSO consists of two pivotal phases global and local search that collectively contribute to its efficiency and efficacy. Experimental validation demonstrates the TSO algorithm's remarkable performance across various benchmark functions, showcasing its competitive edge in balancing exploration and exploitation within the search space. A distinguishing feature of TSO lies in its simplicity, marked by a minimal requirement for user-defined parameters, making it an accessible yet powerful optimization tool. |
| title | Trochoid Search Optimization |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2312.13597 |