Chaos for optimization
Optimization is the process to find the optimal solution from possible solutions. However, in many practical optimization problems, the number of feasible solutions has exploded with the size of the problem, which makes it impossible to obtain the global optimal solution. Chaos is a kind of random...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/152467 |
| _version_ | 1826119212344541184 |
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| author | Guo, Yao |
| author2 | Wang Lipo |
| author_facet | Wang Lipo Guo, Yao |
| author_sort | Guo, Yao |
| collection | NTU |
| description | Optimization is the process to find the optimal solution from possible solutions. However, in many practical optimization problems, the number of feasible solutions has exploded with the size of the problem, which makes it impossible to obtain the global optimal solution.
Chaos is a kind of random-like movement generated from a nonlinear system. Many researchers demonstrate that a series of nonlinear characteristics of chaos, ergodicity, non-periodic, randomness, etc., can improve the randomness and speed when searching optimal solutions within a short time.
This dissertation presents a state-of-the-art review of chaos for optimization. The related works are reviewed from chaotic approaches and optimization applications.
Then simulation experiments of several chaos optimization algorithms are given in detail. Finally, the superiority of chaos and two factors affecting performance are discussed, as well as some future research directions are provided. |
| first_indexed | 2024-10-01T04:56:29Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/152467 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:56:29Z |
| publishDate | 2021 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1524672023-07-04T16:47:55Z Chaos for optimization Guo, Yao Wang Lipo School of Electrical and Electronic Engineering ELPWang@ntu.edu.sg Engineering::Computer science and engineering::Theory of computation::Analysis of algorithms and problem complexity Optimization is the process to find the optimal solution from possible solutions. However, in many practical optimization problems, the number of feasible solutions has exploded with the size of the problem, which makes it impossible to obtain the global optimal solution. Chaos is a kind of random-like movement generated from a nonlinear system. Many researchers demonstrate that a series of nonlinear characteristics of chaos, ergodicity, non-periodic, randomness, etc., can improve the randomness and speed when searching optimal solutions within a short time. This dissertation presents a state-of-the-art review of chaos for optimization. The related works are reviewed from chaotic approaches and optimization applications. Then simulation experiments of several chaos optimization algorithms are given in detail. Finally, the superiority of chaos and two factors affecting performance are discussed, as well as some future research directions are provided. Master of Science (Computer Control and Automation) 2021-08-17T06:24:42Z 2021-08-17T06:24:42Z 2021 Thesis-Master by Coursework Guo, Y. (2021). Chaos for optimization. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/152467 https://hdl.handle.net/10356/152467 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Theory of computation::Analysis of algorithms and problem complexity Guo, Yao Chaos for optimization |
| title | Chaos for optimization |
| title_full | Chaos for optimization |
| title_fullStr | Chaos for optimization |
| title_full_unstemmed | Chaos for optimization |
| title_short | Chaos for optimization |
| title_sort | chaos for optimization |
| topic | Engineering::Computer science and engineering::Theory of computation::Analysis of algorithms and problem complexity |
| url | https://hdl.handle.net/10356/152467 |
| work_keys_str_mv | AT guoyao chaosforoptimization |