High-dimensional optimization presents a novel challenge within the realm of intelligent
computing, necessitating innovative approaches. When tackling high-dimensional spaces …

High-dimensional optimization has numerous potential applications in both academia and
industry. It is a major challenge for optimization algorithms to generate very accurate …

This article presents a surrogate-assisted multiswarm optimization (SAMSO) algorithm for
high-dimensional computationally expensive problems. The proposed algorithm includes …

High-dimensional optimization problems are increasingly pervasive in real-world
applications nowadays and become harder and harder to optimize due to increasingly …

Particle swarm optimization (PSO) is one recently proposed population-based stochastic
optimization technique, and gradient-based descent methods are efficient local optimization …

As the number of dimensions of an optimization problem increases, the process of deriving
the solution becomes more complicated and difficult. Metaheuristic algorithms are effective …

In this paper, a surrogate-assisted hybrid swarm optimization algorithm is proposed to solve
high-dimensional computationally expensive problems. Two swarms are, respectively, used …

High-dimensional optimization problems are more and more common in the era of big data
and the Internet of things (IoT), which seriously challenge the optimization performance of …

Feature selection (FS) is an adequate data pre-processing method that reduces the
dimensionality of datasets and is used in bioinformatics, finance, and medicine. Traditional …

High dimensional optimization problems play an important role in many complex
engineering area. Though many variants of particle swarm optimization (PSO) have been …