An efficient evolutionary algorithm based on deep reinforcement learning for large-scale sparse multiobjective optimization

Gao, Mingsheng; Feng, Xiang; Yu, Huiqun; Li, Xiuquan

doi:10.1007/s10489-023-04574-9

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2024

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Cited by 5 publications

References 57 publications

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A Non-uniform Clustering Based Evolutionary Algorithm for Solving Large-Scale Sparse Multi-objective Optimization Problems

Shao,

Tian,

Zhang

2024

Communications in Computer and Information Science

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A Non-uniform Clustering Based Evolutionary Algorithm for Solving Large-Scale Sparse Multi-objective Optimization Problems

Shao,

Tian,

Zhang

2024

Communications in Computer and Information Science

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Multi-objective optimization enabling CFRP energy-efficient milling based on deep reinforcement learning

Zhang,

Yan

et al. 2024

Appl Intell

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Improving two-layer encoding of evolutionary algorithms for sparse large-scale multiobjective optimization problems

Jiang,

Wang,

Hong

et al. 2024

Complex Intell. Syst.

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Sparse large-scale multiobjective problems (LSMOPs) are characterized as an NP-hard issue that undergoes a significant presence of zero-valued variables in Pareto optimal solutions. In solving sparse LSMOPs, recent studies typically employ a specialized two-layer encoding, where the low-level layer undertakes the optimization of zero variables and the high-level layer is in charge of non-zero variables. However, such an encoding usually puts the low-level layer in the first place and thus cannot achieve a balance between optimizing zero and non-zero variables. To this end, this paper proposes to build a two-way association between the two layers using a mutual preference calculation method and a two-way matching strategy. Essentially, the two-way association balances the influence of two layers on the encoded individual by relaxing the control of the low-level layer and enhancing the control of the high-level layer, thus reaching the balance between the optimizations of zero and non-zero variables. Moreover, we propose a new evolutionary algorithm equipped with the modules and compare it with several state-of-the-art algorithms on 32 benchmark problems. Extensive experiments verify its effectiveness, as the proposed modules can improve the two-layer encoding and help the algorithm achieve superior performance on sparse LSMOPs.

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An efficient evolutionary algorithm based on deep reinforcement learning for large-scale sparse multiobjective optimization

Cited by 5 publications

References 57 publications

A Non-uniform Clustering Based Evolutionary Algorithm for Solving Large-Scale Sparse Multi-objective Optimization Problems

A Non-uniform Clustering Based Evolutionary Algorithm for Solving Large-Scale Sparse Multi-objective Optimization Problems

Multi-objective optimization enabling CFRP energy-efficient milling based on deep reinforcement learning

Improving two-layer encoding of evolutionary algorithms for sparse large-scale multiobjective optimization problems

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