Jianchao Zeng

TL;DR: Empirical studies demonstrate that the proposed surrogate-assisted cooperative swarm optimization algorithm is able to find high-quality solutions for high-dimensional problems on a limited computational budget.

TL;DR: This paper proposes a surrogate-assisted hierarchical particle swarm optimizer for high-dimensional problems consisting of a standard particle swarm optimization (PSO) algorithm and a social learning particle Swarm optimization algorithm (SL-PSO), where the PSO and SL- PSO work together to explore and exploit the search space, and simultaneously enhance the global and local performance of the surrogate model.

TL;DR: The performance of the proposed TLSAPSO algorithm is examined on ten widely used benchmark problems, and the experimental results show that the proposed algorithm is effective and highly competitive with the state-of-the-art, especially for multimodal optimization problems.

TL;DR: A multiobjective infill criterion (MIC) that considers the approximated fitness and the approximation uncertainty as two objectives is proposed for a GP-assisted social learning particle swarm optimization algorithm and is shown to be particularly important for high-dimensional problems, where the estimated uncertainty becomes less reliable.

TL;DR: An improved vector particle swarm optimization (IVPSO) algorithm is proposed to solve COPs, based on the simple constraint-preserving method, and the performance of IVPSO is tested on 13 well-known benchmark functions.