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

Abstract EN

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.