Surrogate-assisted fully-informed particle swarm optimization for high-dimensional expensive optimization

Abstract

Surrogate-Assisted Evolutionary Algorithms (SAEAs) have been proven to be powerful optimization tools for tackling Expensive Optimization Problems (EOPs) where a limited number of function evaluations are available. However, many SAEAs are only designed for low- or medium-dimensional EOPs. Existing SAEAs are challenging to address High-dimensional EOPs (HEOPs) owing to the curse of dimensionality and lack of powerful exploitation capacity. To tackle HEOPs efficiently, a Surrogate-Assisted Fully-informed Particle Swarm Optimization (SA-FPSO) algorithm is proposed in this paper. Firstly, a generation-based Social Learning-based PSO (SLPSO) is adopted to explore the whole decision space with the help of the global surrogate model. Secondly, the fully-informed search scheme is incorporated into the framework of SLPSO to improve its exploitation capacity in the surrogate-assisted search environment. Thirdly, a local space identification strategy is proposed to determine the search range for the local surrogate-assisted search. Seven commonly used expensive benchmark functions with dimensions ranging from 30D to 300D are used to verify the performance of SA-FPSO for HEOPs. Experiment results indicate that SA-FPSO obtains superior performance over several state-of-the-art SAEAs both in terms of convergence speed and solution accuracy.