Evolutionary Multi/Many-Objective Optimisation via Bilevel DecompositionOACSTPCDEI

Decomposition of a complex multi-objective optimi-sation problem(MOP)to multiple simple subMOPs,known as M2M for short,is an effective approach to multi-objective opti-misation.However,M2M facilitates little communication/collabo-ration between subMOPs,which limits its use in complex optimi-sation scenarios.This paper extends the M2M framework to develop a unified algorithm for both multi-objective and many-objective optimisation.Through bilevel decomposition,an MOP is divided into multiple subMOPs at upper level,each of which is further divided into a number of single-objective subproblems at lower level.Neighbouring subMOPs are allowed to share some subproblems so that the knowledge gained from solving one sub-MOP can be transferred to another,and eventually to all the sub-MOPs.The bilevel decomposition is readily combined with some new mating selection and population update strategies,leading to a high-performance algorithm that competes effectively against a number of state-of-the-arts studied in this paper for both multi-and many-objective optimisation.Parameter analysis and compo-nent analysis have been also carried out to further justify the pro-posed algorithm.