Swarm-Intelligence Optimization Method for Dynamic Optimization Problem
In recent years, the vigorous rise in computational intelligence has opened up new research ideas for solving chemical dynamic optimization problems, making the application of swarm-intelligence optimization techniques more and more widespread. However, the potential for algorithms with different pe...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-05-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/10/11/1803 |
| _version_ | 1827664785056989184 |
|---|---|
| author | Rui Liu Yuanbin Mo Yanyue Lu Yucheng Lyu Yuedong Zhang Haidong Guo |
| author_facet | Rui Liu Yuanbin Mo Yanyue Lu Yucheng Lyu Yuedong Zhang Haidong Guo |
| author_sort | Rui Liu |
| collection | DOAJ |
| description | In recent years, the vigorous rise in computational intelligence has opened up new research ideas for solving chemical dynamic optimization problems, making the application of swarm-intelligence optimization techniques more and more widespread. However, the potential for algorithms with different performances still needs to be further investigated in this context. On this premise, this paper puts forward a universal swarm-intelligence dynamic optimization framework, which transforms the infinite-dimensional dynamic optimization problem into the finite-dimensional nonlinear programming problem through control variable parameterization. In order to improve the efficiency and accuracy of dynamic optimization, an improved version of the multi-strategy enhanced sparrow search algorithm is proposed from the application side, including good-point set initialization, hybrid algorithm strategy, Lévy flight mechanism, and Student’s <i>t</i>-distribution model. The resulting augmented algorithm is theoretically tested on ten benchmark functions, and compared with the whale optimization algorithm, marine predators algorithm, harris hawks optimization, social group optimization, and the basic sparrow search algorithm, statistical results verify that the improved algorithm has advantages in most tests. Finally, the six algorithms are further applied to three typical dynamic optimization problems under a universal swarm-intelligence dynamic optimization framework. The proposed algorithm achieves optimal results and has higher accuracy than methods in other references. |
| first_indexed | 2024-03-10T01:07:40Z |
| format | Article |
| id | doaj.art-af09eaae23ae42eca0a7b9beffc82be0 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T01:07:40Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-af09eaae23ae42eca0a7b9beffc82be02023-11-23T14:24:48ZengMDPI AGMathematics2227-73902022-05-011011180310.3390/math10111803Swarm-Intelligence Optimization Method for Dynamic Optimization ProblemRui Liu0Yuanbin Mo1Yanyue Lu2Yucheng Lyu3Yuedong Zhang4Haidong Guo5School of Electronic Information, Guangxi Minzu University, Nanning 530006, ChinaGuangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Guangxi Minzu University, Nanning 530006, ChinaSchool of Chemistry and Chemical Engineering, Guangxi Minzu University, Nanning 530006, ChinaInstitute of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, ChinaSchool of Electronic Information, Guangxi Minzu University, Nanning 530006, ChinaInstitute of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, ChinaIn recent years, the vigorous rise in computational intelligence has opened up new research ideas for solving chemical dynamic optimization problems, making the application of swarm-intelligence optimization techniques more and more widespread. However, the potential for algorithms with different performances still needs to be further investigated in this context. On this premise, this paper puts forward a universal swarm-intelligence dynamic optimization framework, which transforms the infinite-dimensional dynamic optimization problem into the finite-dimensional nonlinear programming problem through control variable parameterization. In order to improve the efficiency and accuracy of dynamic optimization, an improved version of the multi-strategy enhanced sparrow search algorithm is proposed from the application side, including good-point set initialization, hybrid algorithm strategy, Lévy flight mechanism, and Student’s <i>t</i>-distribution model. The resulting augmented algorithm is theoretically tested on ten benchmark functions, and compared with the whale optimization algorithm, marine predators algorithm, harris hawks optimization, social group optimization, and the basic sparrow search algorithm, statistical results verify that the improved algorithm has advantages in most tests. Finally, the six algorithms are further applied to three typical dynamic optimization problems under a universal swarm-intelligence dynamic optimization framework. The proposed algorithm achieves optimal results and has higher accuracy than methods in other references.https://www.mdpi.com/2227-7390/10/11/1803dynamic optimizationswarm intelligencecontrol variable parameterizationnonlinear programming problemsparrow search algorithm |
| spellingShingle | Rui Liu Yuanbin Mo Yanyue Lu Yucheng Lyu Yuedong Zhang Haidong Guo Swarm-Intelligence Optimization Method for Dynamic Optimization Problem Mathematics dynamic optimization swarm intelligence control variable parameterization nonlinear programming problem sparrow search algorithm |
| title | Swarm-Intelligence Optimization Method for Dynamic Optimization Problem |
| title_full | Swarm-Intelligence Optimization Method for Dynamic Optimization Problem |
| title_fullStr | Swarm-Intelligence Optimization Method for Dynamic Optimization Problem |
| title_full_unstemmed | Swarm-Intelligence Optimization Method for Dynamic Optimization Problem |
| title_short | Swarm-Intelligence Optimization Method for Dynamic Optimization Problem |
| title_sort | swarm intelligence optimization method for dynamic optimization problem |
| topic | dynamic optimization swarm intelligence control variable parameterization nonlinear programming problem sparrow search algorithm |
| url | https://www.mdpi.com/2227-7390/10/11/1803 |
| work_keys_str_mv | AT ruiliu swarmintelligenceoptimizationmethodfordynamicoptimizationproblem AT yuanbinmo swarmintelligenceoptimizationmethodfordynamicoptimizationproblem AT yanyuelu swarmintelligenceoptimizationmethodfordynamicoptimizationproblem AT yuchenglyu swarmintelligenceoptimizationmethodfordynamicoptimizationproblem AT yuedongzhang swarmintelligenceoptimizationmethodfordynamicoptimizationproblem AT haidongguo swarmintelligenceoptimizationmethodfordynamicoptimizationproblem |