التفاصيل البيبلوغرافية
| العنوان: |
Optimizing High-Dimensional Functions with an Efficient Particle Swarm Optimization Algorithm |
| المؤلفون: |
Guoliang Li, Jinhong Sun, Mohammad N.A. Rana, Yinglei Song, Chunmei Liu, Zhi-yu Zhu |
| المصدر: |
Hindawi, Mathematical Problems in Engineering. 2020:1-10 |
| سنة النشر: |
2020 |
| الوصف: |
The optimization of high-dimensional functions is an important problem in both science and engineering. Particle swarm optimization is a technique often used for computing the global optimum of a multivariable function. In this paper, we develop a new particle swarm optimization algorithm that can accurately compute the optimal value of a high-dimensional function. The iteration process of the algorithm is comprised of a number of large iteration steps, where a large iteration step consists of two stages. In the first stage, an expansion procedure is utilized to effectively explore the high-dimensional variable space. In the second stage, the traditional particle swarm optimization algorithm is employed to compute the global optimal value of the function. A translation step is applied to each particle in the swarm after a large iteration step is completed to start a new large iteration step. Based on this technique, the variable space of a function can be extensively explored. Our analysis and testing results on high-dimensional benchmark functions show that this algorithm can achieve optimization results with significantly improved accuracy, compared with traditional particle swarm optimization algorithms and a few other state-of-the-art optimization algorithms based on particle swarm optimization. |
| نوع الوثيقة: |
redif-article |
| اللغة: |
English |
| DOI: |
10.1155/2020/5264547 |
| الإتاحة: |
https://ideas.repec.org/a/hin/jnlmpe/5264547.html |
| رقم الأكسشن: |
edsrep.a.hin.jnlmpe.5264547 |
| قاعدة البيانات: |
RePEc |
