Optimizing High-Dimensional Functions with an Efficient Particle Swarm Optimization Algorithm

• Published in: Mathematical problems in engineering 2020, Vol.2020 (2020), p.1-10
• Main Authors: Liu, Chunmei, Song, Yinglei, Rana, Mohammad N.A., Sun, Jinhong, Li, Guoliang, Zhu, Zhiyu
• Format: Article
• Language: English
• Subjects: Algorithms; Atoms & subatomic particles; Iterative methods; Optimization algorithms; Optimization techniques; Particle swarm optimization; Proteins; Velocity
• ISSN: 1024-123X; 1563-5147
• DOI: 10.1155/2020/5264547

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.