A Surrogate-Assisted Clustering Particle Swarm Optimizer for Expensive Optimization Under Dynamic Environment

In recent years, surrogate-assisted evolutionary algorithms have been developed for expensive optimization. However, a majority of applications are dynamic optimization problems in the real-world. In this paper, therefore, a surrogate-assisted clustering particle swarm optimizer is proposed for expensive dynamic optimization. In the proposed method, several clusters are first created by affinity propagation clustering, and then local radial basis function (RBF) surrogates are built based on the neighbor evaluated points for each cluster. Finally, in each cluster, the local RBF assists particle swarm optimizer to search the most promising point, which is evaluated by real objective function. To track dynamic environment, the points with best exact fitness in each cluster are added into new cradle swarm, if environmental change has occurred. A variety of experiments have been conducted on the moving peaks benchmark (MPB) with 500 change frequency in each environment. The experimental results have demonstrated that the proposed approach has a good performance.