Considering spatiotemporal evolutionary information in dynamic multi‐objective optimisation

Abstract

AbstractPreserving population diversity and providing knowledge, which are two core tasks in the dynamic multi‐objective optimisation (DMO), are challenging since the sampling space is time‐ and space‐varying. Therefore, the spatiotemporal property of evolutionary information needs to be considered in the DMO. In the present study, a sliding‐time‐window‐based population clustering method (SPC) is proposed to effectively solve dynamic multi‐objective optimisation problems (DMOPs). In the SPC, the knowledge is provided by saving historical data in the temporal dimension, and a spectral clustering method is used to divide the saved data into multiple neighbourhood subspaces for preserving population diversity in the spatial dimension. The SPC is incorporated into the RM‐MEDA and is compared with other recently proposed state‐of‐the‐art dynamic multi‐objective evolutionary algorithms (DMOEAs) on 14 DMOPs introduced in IEEE CEC2018. Simulation results demonstrate that the proposed method is capable of enhancing the tracking performance of the RM‐MEDA in the dynamically changing environments. Additionally, the SPC is utilised to solve an actual dynamic multi‐objective translation control problem of an immersed tunnel element. Results show that the proposed SPC outperforms the knee point‐based transfer learning method in terms of both computational cost and tracking performance.