Multi-Objective Optimization Using Cooperative Garden Balsam Optimization with Multiple Populations

Traditional multi-objective evolutionary algorithms (MOEAs) consider multiple objectives as a whole when solving multi-objective optimization problems (MOPs). In this paper, the hybridization of garden balsam optimization (GBO) is presented to solve multi-objective optimization, applying multiple populations for multiple objectives individually. Moreover, in order to improve the diversity of the solutions, both crowding distance computations and epsilon dominance relations are adopted when updating the archive. Furthermore, an efficient selection procedure called co-evolutionary multi-swarm garden balsam optimization (CMGBO) is proposed to ensure the convergence of well-diversified Pareto regions. The performance of the used algorithm is validated on 12 test functions. The algorithm is employed to solve four real-world problems in engineering. The achieved consequences corroborate the advantage of the proposed algorithm with regard to convergence and diversity

Multi-Objective Optimization Using Cooperative Garden Balsam Optimization with Multiple Populations

Traditional multi-objective evolutionary algorithms (MOEAs) consider multiple objectives as a whole when solving multi-objective optimization problems (MOPs). In this paper, the hybridization of garden balsam optimization (GBO) is presented to solve multi-objective optimization, applying multiple populations for multiple objectives individually. Moreover, in order to improve the diversity of the solutions, both crowding distance computations and epsilon dominance relations are adopted when updating the archive. Furthermore, an efficient selection procedure called co-evolutionary multi-swarm garden balsam optimization (CMGBO) is proposed to ensure the convergence of well-diversified Pareto regions. The performance of the used algorithm is validated on 12 test functions. The algorithm is employed to solve four real-world problems in engineering. The achieved consequences corroborate the advantage of the proposed algorithm with regard to convergence and diversity

Research on the Ranking of University Education based on Grey-TOPSIS-DEA Method

The University is the cradle of the higher education. In the college life, the college students continue to accept the new knowledge and continue to grow. Obviously, the level of the university education will be directly related to the growth for the college students. Therefore, it is an important job to evaluate and order the college education quality. In this paper, we combine the Grey theory, TOPSIS with DEA method. And we propose an improved Grey-TOPSIS-DEA model. Then, we use the model to evaluate the college education quality. Finally, we get the rankings of the college education. In the last part of this paper, we use the method to evaluate the education quality for different colleges. And we verify the validity of the method

Study of mining method of signal of coal and gas outburst

For problem of inaccurate detection of characteristic signal in coal mining operation because it is difficult to capture nonlinear stochastic variation of abnormal signal with traditional associated clustering algorithm, a mining method of signal of coal and gas outburst based on feature-based association mining algorithm was proposed. The method uses wavelet transform to extract status signal characteristics of coal mine work area to provide a basis for signal mining of coal and gas outburst, and calculates degree of association between the status signal characteristics of coal mine work area to achieve mining of signal of coal and gas outburst. The experimental results show that the method can improve accuracy of mining of signal of coal and gas outburst

Surface Defect Recognition of Solar Panel Based on Percolation-Based Image Processing and Serre Standard Model

During the production process of solar panels, it is inevitable to have some defects, such as cracks on the surface of solar panels due to extrusion or damage due to quality issues. This article improves the Serre standard model, which can simulate the ventral visual pathway with object recognition ability, based on the latest research progress and results of simulating biological visual mechanism models in computer vision, to improve the recognition effect of surface defects on solar panels. At the same time, a pre-processing scheme combining Gaussian Laplace operator operator and adaptive Wiener filter to remove noise spots is studied, and the local Gabor Binary Pattern Histogram Sequence (LGBPHS) features are obtained through pre-processing. The Percolation-Based image processing method for detecting obvious cracks was used to determine the location of the algorithm and the calculation results based on the improved standard model method. It mainly refers to the MAX value output by the C2 layer and the classification and identification results of whether there are cracks, and the crack location function is completed. The experimental results show that the proposed method has an accuracy rate of 98.86% in training and 98.64% in testing, and both the false detection rate and the missed detection rate do not exceed 1%. Therefore, the method proposed in the study has a high accuracy and can effectively identify the surface defects of solar panels

Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study

ObjectiveTo create and validate a simple and transferable machine learning model from electronic health record data to accurately predict clinical deterioration in patients with covid-19 across institutions, through use of a novel paradigm for model development and code sharing.DesignRetrospective cohort study.SettingOne US hospital during 2015-21 was used for model training and internal validation. External validation was conducted on patients admitted to hospital with covid-19 at 12 other US medical centers during 2020-21.Participants33 119 adults (≥18 years) admitted to hospital with respiratory distress or covid-19.Main outcome measuresAn ensemble of linear models was trained on the development cohort to predict a composite outcome of clinical deterioration within the first five days of hospital admission, defined as in-hospital mortality or any of three treatments indicating severe illness: mechanical ventilation, heated high flow nasal cannula, or intravenous vasopressors. The model was based on nine clinical and personal characteristic variables selected from 2686 variables available in the electronic health record. Internal and external validation performance was measured using the area under the receiver operating characteristic curve (AUROC) and the expected calibration error-the difference between predicted risk and actual risk. Potential bed day savings were estimated by calculating how many bed days hospitals could save per patient if low risk patients identified by the model were discharged early.Results9291 covid-19 related hospital admissions at 13 medical centers were used for model validation, of which 1510 (16.3%) were related to the primary outcome. When the model was applied to the internal validation cohort, it achieved an AUROC of 0.80 (95% confidence interval 0.77 to 0.84) and an expected calibration error of 0.01 (95% confidence interval 0.00 to 0.02). Performance was consistent when validated in the 12 external medical centers (AUROC range 0.77-0.84), across subgroups of sex, age, race, and ethnicity (AUROC range 0.78-0.84), and across quarters (AUROC range 0.73-0.83). Using the model to triage low risk patients could potentially save up to 7.8 bed days per patient resulting from early discharge.ConclusionA model to predict clinical deterioration was developed rapidly in response to the covid-19 pandemic at a single hospital, was applied externally without the sharing of data, and performed well across multiple medical centers, patient subgroups, and time periods, showing its potential as a tool for use in optimizing healthcare resources