1. An Improved Pelican Optimization Algorithm Based on Chaos Mapping Factor.
- Author
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Yutong Li, Yu Liu, Chunli Lin, Jiayao Wen, Pengguo Yan, and Yukun Wang
- Subjects
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OPTIMIZATION algorithms , *PRESSURE vessels , *ENGINEERING design , *METAHEURISTIC algorithms , *BLUEGRASSES (Plants) - Abstract
The Pelican Optimization Algorithm (POA) is a meta-heuristic algorithm that emulates the behavior and hunting strategy of pelicans. This paper presents three modifications that enhance the algorithmic traversal of POA by incorporating ten chaotic mapping factors. These modifications aim to improve the efficiency and accuracy of the traversal process. Firstly, the pelican optimization algorithm with chaotic mapping factors added to population generation but random prey generation (COPOA); secondly, the pelican optimization algorithm with chaotic mapping factors applied to prey generation but random population generation (OCPOA); finally, the pelican optimization algorithm with the same chaotic mapping factor, different sequences generated by different random initial values are mapped to the population and prey generation (CCPOA). The results of simulations on 12 benchmark test functions demonstrate that the algorithm (COPOA) adding the chaotic mapping factor to the population generation alone has higher generalizability, convergence speed and finding accuracy compared with the standard POA algorithm. Among these modifications, the Sine-COPOA method performed the best in ten chaotic mapping factor comparison tests. Furthermore, a comparison is made between the Sine-COPOA and typical intelligent optimization algorithms to assess its competence in optimization. The results show that Sine-COPOA performs better in most of the 12 benchmark test functions. Finally, the engineering design problems (pressure vessel problem and 10-bar truss design) are optimized. The experimental results demonstrate that Sine-COPOA effectively solves both function optimization and engineering optimization problems. [ABSTRACT FROM AUTHOR]
- Published
- 2023