Research on Adaptive Particle Swarm Optimization Algorithm Based on Diversity-Driven Optimization

A novel Diversity Actuate Adaptive Particle Swarm Optimization (DAAPSO) algorithm is introduced to enhance convergence speed and address premature convergence challenges in particle swarm optimization. Integrating an inertia weight linear decline mechanism with a diversity-driven speed strategy, DAA...

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Bibliographic Details
Published in2024 5th International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) pp. 1039 - 1042
Main Authors Wang, Peishuo, Zuo, Jialiang, Zhang, Zhihao, Lei, Pengfei
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.06.2024
Subjects
Boosting
Classification algorithms
Convergence
DAAPSO algorithm
Diversity driven
Diversity reception
Feature extraction
linearly decreasing inertia weight
Optimization
Particle swarm optimization
Smoothing methods
Splines (mathematics)
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Summary:A novel Diversity Actuate Adaptive Particle Swarm Optimization (DAAPSO) algorithm is introduced to enhance convergence speed and address premature convergence challenges in particle swarm optimization. Integrating an inertia weight linear decline mechanism with a diversity-driven speed strategy, DAAPSO effectively balances exploration and exploitation. Empirical studies demonstrate the superior performance of DAAPSO across various dimensions compared to existing algorithms. Specifically, experiments on curve energy smoothing and feature selection indicate that DAAPSO significantly improves the smoothness of B-spline curves, reduces curvature peaks post-smoothing, and enhances the accuracy of 5nearest neighbor classifiers when compared to alternative feature selection methods. These results highlight the robust optimization capabilities of DAAPSO, showcasing its effectiveness in both benchmark functions and real-world applications. The findings underscore the efficiency, practicality, and broad applicability of the DAAPSO algorithm.
DOI:10.1109/AIEA62095.2024.10692728