An adaptive model switch-based surrogate-assisted evolutionary algorithm for noisy expensive multi-objective optimization

AbstractTo solve noisy and expensive multi-objective optimization problems, there are only a few function evaluations can be used due to the limitation of time and/or money. Because of the influence of noises, the evaluations are inaccurate. It is challenging for the existing surrogate-assisted evolutionary algorithms. Due to the influence of noises, the performance of the surrogate model constructed by these algorithms is degraded. At the same time, noises would mislead the evolution direction. More importantly, because of the limitations of function evaluations, noise treatment methods consuming many function evaluations cannot be applied. An adaptive model switch-based surrogate-assisted evolutionary algorithm is proposed to solve such problems in this paper. The algorithm establishes radial basis function networks for denoising. An adaptive model switch strategy is adopted to select suited surrogate model from Gaussian regression and radial basis function network. It adaptively selects the sampling strategies based on the maximum improvement in the convergence, diversity, and approximation uncertainty to make full use of the limited number of function evaluations. The experimental results on a set of test problems show that the proposed algorithm is more competitive than the five most advanced surrogate-assisted evolutionary algorithms.</jats:p

Effectiveness Evaluation of Financing Platform Operation of Buildings Energy Saving Transformation Using ANP-Fuzzy in China: An Empirical Study

Building energy saving transformation is an inevitable requirement to achieve sustainable development, which can bring considerable economic, environmental, and social benefits. The key to healthy development of the market lies in the orderly operation of the financing platform. The effectiveness of the financing platform depends on scientific evaluation. Therefore, it is necessary to design a set of systematic and practical evaluation indicators for operational effectiveness of the buildings energy saving transformation financing platform, so as to provide reference for the effective operation of the financing platform, and provide measurement means for scholars to conduct quantitative research on the financing platform. This paper analyzes the effectiveness evaluation content for the financing platform operation of buildings energy saving transformation from the two levels of operation mechanism and operation subject behavior. Combined with the particularity of the financing platform of building energy saving transformation, the operational effectiveness evaluation index system of the financing platform is designed from three levels. The Analytic Network Process (ANP) method is applied to construct network structure, to describe element correlation, and to calculate index weight. The fuzzy comprehensive evaluation (Fuzzy) method was used to carry out quantitative evaluation of qualitative indicators. The Energy Performance Contracting (EPC) financing platform in Beijing was taken as an example to make an empirical analysis. The results show that the operational effectiveness evaluation system of the financing platform of buildings energy saving transformation constructed in this paper has certain practicability. In this evaluation system, scores of target consistency, the degree of information sharing among departments and coordination of operation mechanism are low. Finally, some policy suggestions are put forward to optimize financing platform of buildings energy saving transformation in China

Effectiveness Evaluation of Financing Platform Operation of Buildings Energy Saving Transformation Using ANP-Fuzzy in China: An Empirical Study

Building energy saving transformation is an inevitable requirement to achieve sustainable development, which can bring considerable economic, environmental, and social benefits. The key to healthy development of the market lies in the orderly operation of the financing platform. The effectiveness of the financing platform depends on scientific evaluation. Therefore, it is necessary to design a set of systematic and practical evaluation indicators for operational effectiveness of the buildings energy saving transformation financing platform, so as to provide reference for the effective operation of the financing platform, and provide measurement means for scholars to conduct quantitative research on the financing platform. This paper analyzes the effectiveness evaluation content for the financing platform operation of buildings energy saving transformation from the two levels of operation mechanism and operation subject behavior. Combined with the particularity of the financing platform of building energy saving transformation, the operational effectiveness evaluation index system of the financing platform is designed from three levels. The Analytic Network Process (ANP) method is applied to construct network structure, to describe element correlation, and to calculate index weight. The fuzzy comprehensive evaluation (Fuzzy) method was used to carry out quantitative evaluation of qualitative indicators. The Energy Performance Contracting (EPC) financing platform in Beijing was taken as an example to make an empirical analysis. The results show that the operational effectiveness evaluation system of the financing platform of buildings energy saving transformation constructed in this paper has certain practicability. In this evaluation system, scores of target consistency, the degree of information sharing among departments and coordination of operation mechanism are low. Finally, some policy suggestions are put forward to optimize financing platform of buildings energy saving transformation in China.</jats:p

Parameter-efficient weakly supervised referring video object segmentation via chain-of-thought reasoning

Abstract Referring video object segmentation (RVOS) aims to segment the object corresponding to a language expression in a video. Most existing RVOS methods are trained using accurate per-pixel annotations, which are expensive and time-consuming to obtain. Moreover, they need to update the entire parameter of a segmentation model, making it inefficient to train as the model scale increases. In this paper, we propose a novel parameter-efficient framework under weak supervision, dubbed ReferringAdapter, to ameliorate both of issues. Specifically, we propose to adapt an off-the-shelf image segmentation model for RVOS by plugging a small set of trained parameters, i.e., an adapter, into the intermediate layer. This efficiently endows a uni-modal image segmentation model with the cross-modal ability to segment the video object referred by a language expression. To update the adapter parameters under weak supervision, instead of directly fuse the video and sentence-level language features, we propose chain-of-thought reasoning to consider the intermediate steps along the thought process. Extensive experiments demonstrate that training the adapter with 1.1% of total parameters can outperform previous weakly supervised methods by 11.6 $$-$$ - 15.3 mAP and achieve comparable performance with fully supervised ones