Social Learning Particle Swarm Optimization with Two-surrogate Collaboration for Offline Data-driven Multiobjective Optimization

Data-driven evolutionary algorithms (DDEAs) have shown strong capacity in solving optimization problems with the help of surrogate models. However, current DDEAs often fall into the trap of inaccurate surrogates if no additional data can be obtained for the surrogates update during the optimization process. In addition, when dealing with multiobjective optimization problems, the surrogates in DDEAs will further suffer the difficulty of aggravating cumulative predicted fitness error. To overcome these difficulties, we propose a two-surrogate collaboration (TSC) management method, in which a Kriging surrogate and a radial basis function network (RBFN) surrogate are adopted to approximate the real fitness evaluation for the parent solutions and the newly generated offspring solutions, respectively. This TSC management method can effectively reduce the prediction uncertainty and improve the prediction performance of each surrogate without any other surrogate update process. During the optimization process, we use a modified social learning particle swarm optimization (SLPSO) as the basic search method and propose an offline DDEA. With the help of TSC, our resulting SLPSO-TSC algorithm can search for potential optimal solutions quickly and effectively without the help of any real fitness evaluation during the optimization process. The performance of the proposed SLPSO-TSC algorithm is verified on eleven widely used benchmark problems and compared with five different offline DDEAs. The experimental results show the high competitiveness of our proposed SLPSO-TSC for offline data-driven multiobjective optimization.