A clustering and dimensionality reduction based evolutionary algorithm for large-scale multi-objective problems

When solving multi-objective problems (MOPs) with a large number of variables, analysis of the linkage between decision variables is maybe useful for avoiding "the curse of dimensionality". In this work, a clustering and dimensionality reduction based evolutionary algorithm for large-scale multi-objective problems is suggested, which focuses on clustering decision variables into two categories and then utilizes a dimensionality reduction approach to get a lower dimensional representation for those variables that affect the convergence of the evolution. The interdependence analysis is carried out next aiming to decompose the convergence variables into a number of subcomponents that are easier to be tackled. The algorithm presented in this article is promising on a series of test functions, and the outcome of these experiments reveal that our suggested algorithm is able to prominently enhance the performance; meanwhile it can save computing costs to a large extent compared with some latest evolutionary algorithms (EAs). In addition, the proposed algorithm can be extended to solve MOPs with dimensions up to 5000, with a good performance obtained. (C) 2020 Elsevier B.V. All rights reserved.