Optimization of high-dimensional expensive multi-objective problems using multi-mode radial basis functions

Optimization of high-dimensional expensive multi-objective problems using multi-mode radial basis functions

Blog Article

Abstract Numerous surrogate-assisted evolutionary algorithms are developed for multi-objective expensive problems with low dimensions, but scarce works have paid attention to that with high dimensions, i.e., generally more than 30 decision variables.In this paper, we propose a multi-mode radial basis functions-assisted evolutionary algorithm (MMRAEA) for solving high-dimensional expensive multi-objective optimization problems.

To improve the reliability, the proposed algorithm uses radial basis functions based on three modes to cooperate to provide Ball - Senior Tops - Baselayer the qualities and uncertainty information of candidate solutions.Meanwhile, bi-population based on competitive swarm optimizer and genetic algorithm are applied for better exploration and exploitation in high-dimensional search space.Accordingly, an infill criterion based on multi-mode of radial basis functions that comprehensively considers the quality and uncertainty of candidate solutions is proposed.Experimental results on widely-used benchmark problems with up to 100 decision variables AUTOMATICS demonstrate the effectiveness of our proposal.

Furthermore, the proposed method is applied to the structure optimization of the blended-wing-body underwater glider (BWBUG) and gets impressive solutions.