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Considering spatiotemporal evolutionary information in dynamic multi-objective optimisation
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DOI：10.1049/cit2.12249.png)
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Abstract
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Preserving 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.
Keywords:
dynamic multi-objective optimization
dynamic optimization
evolutionary computation
high-performing computing
immersed tunnel element
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