Domain Generalization-Based Dynamic Multiobjective Optimization: A Case Study on Disassembly Line Balancing

Fang, Yilin; Liu, Fubo; Li, Miqing; Cui, Hao

doi:10.1109/tevc.2022.3233642

Cited by 9 publications

(1 citation statement)

References 49 publications

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“…The final result of the DMOP is not a solution but a Pareto optimal set. In the EWC method, the learned task can finally be represented by a set of parameters, and we also want to generate an initial population through a set of parameters [5].…”

Section: The Solution-generative Modelmentioning

confidence: 99%

Dynamic Multi-objective Optimization for Coagulating Process of Carbon Fiber Precursor Based on Lifelong Learning

Liu

Liang

Zhang

et al. 2023

J. Phys.: Conf. Ser.

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Process parameter optimization is an essential link in the coagulating process of carbon fiber. In this paper, considering the dynamic factors of the coagulating bath environment, a dynamic multi-objective optimization problem (DMOP) model for the coagulating process is constructed with process parameters as decision variables and performance indicators as optimization objectives. We combine lifelong learning (LLL) and multi-objective optimization to solve this model and propose a lifelong learning-based dynamic multi-objective evolutionary algorithm (LLL-DMOEA). In LLL-DMOEA, the lifelong learning method is used to learn common knowledge in historical environments. After the arrival of the new environment, we use the common knowledge to generate a high-quality initial population and speed up the process of the multi-objective optimization algorithm to find the Pareto-optimal set (POS) in the new environment. At the same time, common knowledge only requires information in historical environments. The response time to environmental changes can be sped up by learning before the new environment arrives. Experimental results show that the proposed algorithm can effectively solve the optimization problem of process parameters in the coagulating process.

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Section: The Solution-generative Modelmentioning

confidence: 99%

Dynamic Multi-objective Optimization for Coagulating Process of Carbon Fiber Precursor Based on Lifelong Learning

Liu

Liang

Zhang

et al. 2023

J. Phys.: Conf. Ser.

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A prediction method for dynamic multiobjective optimization based on joint subspace and correlation alignment

Li,

Liu,

Deng

2024

Complex Intell. Syst.

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Dynamic multiobjective optimization is a significant challenge in accurately capturing changes in Pareto optimal sets (PS), encompassing both location and manifold changes. Existing approaches primarily focus on tracking changes in the location of the PS, often overlooking the potential impact of changes in the PS manifold, which can be decomposed into rotation and distortion changes. Such oversights can lead to a reduction in the overall performance of an algorithm. To address this issue, a prediction method based on joint subspace and correlation alignment (PSCA) is proposed. PSCA leverages a subspace alignment strategy to effectively capture rotation change in the PS manifold while employing a correlation alignment strategy to capture distortion change. By integrating these two strategies, a quasi-initial population is generated that embodies the captured rotation and distortion change patterns in a new environment. Then, the promising individuals are selected from this quasi-initial population based on their nondominated relations and crowding degree to form the initial population in the new environment. To evaluate the effectiveness of PSCA, we conduct experiments on fourteen benchmark problems. The experimental results demonstrate that PSCA achieves significant improvements over several state-of-the-art algorithms.

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Multi-objective disassembly line balancing and related supply chain management problems under uncertainty: Review and future trends

He,

Chu,

Dolgui

et al. 2024

International Journal of Production Economics

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Domain Generalization-Based Dynamic Multiobjective Optimization: A Case Study on Disassembly Line Balancing

Cited by 9 publications

References 49 publications

Dynamic Multi-objective Optimization for Coagulating Process of Carbon Fiber Precursor Based on Lifelong Learning

Dynamic Multi-objective Optimization for Coagulating Process of Carbon Fiber Precursor Based on Lifelong Learning

A prediction method for dynamic multiobjective optimization based on joint subspace and correlation alignment

Multi-objective disassembly line balancing and related supply chain management problems under uncertainty: Review and future trends

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