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- Title
分段搜索的果蝇算法及其对纺织企业资源配置.
- Authors
白晓波; 邵景峰; 王铁山; 李勃
- Abstract
To solve the multi-parameter and multi-objective optimization problem in the intelligent resource allocation of textile enterprises, Multi-P-LevyFOA is proposed. Firstly, a multi-parameter and multi-objective resource allocation model for intelligent transformation of textile enterprises is established. Then, if the number of iterations is less than or equal to 2/3 of the total number of iterations, based on the random number of Levy flight, the population position is updated to expand the search range and avoid falling into local optimization. When the number of iterations is greater than 2/3 of the total number, the population position is updated with random numbers of uniform distribution, and the search range is narrowed to avoid jumping out of the optimal value range. The global and local optimization ability of the algorithm at different iteration times are analyzed, and the time complexity of Multi-P-LevyFOA algorithm is analyzed and compared with standard FOA and five improved FOA, and its convergence is proven. The performance of Multi-P-LevyFOA is compared with other four improved FOA, the influence of threshold SEP of the algorithm on the 22 benchmark functions is analyzed, and the influence law of β parameter on the optimization effect of the algorithm in Levy flight is studied. Finally, taking the intelligent resource allocation of a textile enterprise in Shaanxi province as an example, the feasibility of Multi-P-LevyFOA is verified. Experimental results show that the Multi-P-LevyFOA can effectively solve the multi-parameter and multi-objective optimization problem under appropriate β parameters, which provides a reference for the intelligent resource allocation of textile enterprises.
- Subjects
SHAANXI Sheng (China); ELECTROTEXTILES; RESOURCE allocation; GLOBAL optimization; MATHEMATICAL optimization; REFERENCE sources; RANDOM numbers; PARTICLE swarm optimization
- Publication
Journal of Frontiers of Computer Science & Technology, 2022, Vol 16, Issue 10, p2330
- ISSN
1673-9418
- Publication type
Article
- DOI
10.3778/j.issn.1673-9418.2203112