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Selecting a subset of important features from a high-dimensional dataset is an important prerequisite for data mining. Meta-heuristic algorithms have gained attention in this field in recent years. The grasshopper optimization algorithm (GOA) is a meta-heuristic algorithm recently proposed based on the migration and hunting of grasshoppers in nature. However, the method suffers from a low diversity of the agents, which results in the stagnation problems, or immature convergence. To make GOA more competent in various situations, this paper stabilizes an improved GOA with new exploratory and exploitative features, which we have called it the SCGOA. The mechanism and structure of the proposed SCGOA are mainly divided into two steps: First, to balance the exploration and exploitation stages, trigonometric substitution is utilized for perturbation of the updating (evolution) of the position vectors of the individuals. Secondly, the diversity of the population is boosted using can Cauchy mutation-based strategy, which can help the grasshopper population to avoid the stagnation and lazy convergence. Therefore, Cauchy mutation is introduced to assist in an adequate variety of the position of the grasshopper population. Performance of SCGOA was validated on the latest IEEE CEC2017 benchmark functions in comparison with several well-known meta-heuristic algorithms. Various extensive results reveal that the proposed SCGOA has achieved a significant advantage over the other rivals. Finally, the Cauchy mutation-based SCGOA was also used for tackling four engineering design problems, and the results showed that SCGOA was superior to some state-of-the-art algorithms. We also developed the binary version of Cauchy mutation-based SCGOA in dealing with many feature selection datasets. The results on feature selection reveal that the binary version can outperform original GOA and other optimization algorithms, with higher classification accuracy, smaller error rate, and less number of features. We think the proposed optimizer can be widely tool for solving forms of the optimization problems. The research will be supported by open access materials and web service for any user guide at https://aliasghaheidari.com .

This paper focuses on the performance of an improved algorithm based on the salp swarm algorithm (SSA), called AGSSA. We planned several new ideas to improve the defects of the original optimizer, such as ease to fall into local optimum and low convergence accuracy. To solve these problems, the SSA algorithm is improved in two parts. Salp swarm algorithm (SSA) is a recently proposed optimization algorithm with advantages and disadvantages, simulating a perception of the salp's foraging and navigation behavior in the deep ocean. The first improvement includes the adaptive control parameter introduced into the follower position update stage, which boosts the local exploitative ability of the population. The second improvement includes the elite gray wolf domination strategy introduced in the last stage of the population position update, which helps the population find the globally optimal solution faster. The performance of AGSSA is verified by a series of problems, including the IEEE CEC2014 benchmark functions, engineering design problems, and feature selection tasks. The experimental results of AGSSA are compared with some well-known metaheuristic algorithms. Simulations reveal that the performance of AGSSA is significantly better than lots of competitive metaheuristic algorithms. Moreover, in solving real-world problems, AGSSA also shows high accuracy in comparison with other metaheuristic algorithms. These points prove that the introduction of the two strategies has a positive effect on the original SSA. Promisingly, the proposed AGSSA can be used as a potential optimization tool in many optimization tasks.

COVID-19 is pervasive and threatens the safety of people around the world. Therefore, now, a method is needed to diagnose COVID-19 accurately. The identification of COVID-19 by X-ray images is a common method. The target area is extracted from the X-ray images by image segmentation to improve classification efficiency and help doctors make a diagnosis. In this paper, we propose an improved crow search algorithm (CSA) based on variable neighborhood descent (VND) and information exchange mutation (IEM) strategies, called VMCSA. The original CSA quickly falls into the local optimum, and the possibility of finding the best solution is significantly reduced. Therefore, to help the algorithm avoid falling into local optimality and improve the global search capability of the algorithm, we introduce VND and IEM into CSA. Comparative experiments are conducted at CEC2014 and CEC'21 to demonstrate the better performance of the proposed algorithm in optimization. We also apply the proposed algorithm to multi-level thresholding image segmentation using Renyi's entropy as the objective function to find the optimal threshold, where we construct 2-D histograms with grayscale images and non-local mean images and maximize the Renyi's entropy on top of the 2-D histogram. The proposed segmentation method is evaluated on X-ray images of COVID-19 and compared with some algorithms. VMCSA has a significant advantage in segmentation results and obtains better robustness than other algorithms. The available extra info can be found at https://github.com/1234zsw/VMCSA.

Image segmentation is an essential pre-processing step and is an indispensable part of image analysis. This paper proposes Renyi's entropy multi-threshold image segmentation based on an improved Slime Mould Algorithm (DASMA). First, we introduce the diffusion mechanism (DM) into the original SMA to increase the population's diversity so that the variants can better avoid falling into local optima. The association strategy (AS) is then added to help the algorithm find the optimal solution faster. Finally, the proposed algorithm is applied to Renyi's entropy multilevel threshold image segmentation based on non-local means 2D histogram. The proposed method's effectiveness is demonstrated on the Berkeley segmentation dataset and benchmark (BSD) by comparing it with some well-known algorithms. The DASMA-based multilevel threshold segmentation technique is also successfully applied to the CT image segmentation of chronic obstructive pulmonary disease (COPD). The experimental results are evaluated by image quality metrics, which show the proposed algorithm's extraordinary performance. This means that it can help doctors analyze the lesion tissue qualitatively and quantitatively, improve its diagnostic accuracy and make the right treatment plan. The supplementary material and info about this article will be available at https://aliasgharheidari.com.

The prediction of foundation settlement is an important topic in loess filling engineering. Based on a filled foundation in Yan’an, China, this study explores the consolidation characteristics of compacted loess with different compaction energy and consolidation pressure through consolidation tests, analyzes the strain-time curve and refines the curve within 2 h, separates the primary and secondary consolidations, and obtains the critical time point between the primary and secondary consolidations. Deformation rate S t ′ and cumulative deformation St were introduced to analyze the S t ′ − St curve at the secondary consolidation stage; the secondary consolidation coefficient was employed to describe the secondary consolidation characteristics of compacted loess. According to the secondary consolidation characteristics, a prediction model of loess settlement considering different compaction energy and fill thickness was proposed, and the applicability of the model was further analyzed. The model will facilitate in guiding the design and construction of loess filling engineering.

In this paper, a gradient-index lens is designed to implement an acoustic Bessel beam. Here, the wave from the point source is reshaped to the Bessel beam with the energy concentrated near the axial direction and almost no divergence. The two-dimensional distribution of the gradient refractive index is obtained based on the analysis of the impedance matching theory. The interface reflection is reduced significantly due to the impedance matching with air. The effect of the acoustic Bessel lens is found to work in a broadband with the use of the subwavelength unit cell and the finite-element simulations. Our results may provide the potential applications for medical ultrasound imaging and signal detection.In this paper, a gradient-index lens is designed to implement an acoustic Bessel beam. Here, the wave from the point source is reshaped to the Bessel beam with the energy concentrated near the axial direction and almost no divergence. The two-dimensional distribution of the gradient refractive index is obtained based on the analysis of the impedance matching theory. The interface reflection is reduced significantly due to the impedance matching with air. The effect of the acoustic Bessel lens is found to work in a broadband with the use of the subwavelength unit cell and the finite-element simulations. Our results may provide the potential applications for medical ultrasound imaging and signal detection.

With the worldwide development of nuclear power plant and requirement of saving energy and resource, high thermal efficiency and economical competitiveness are achieved by using supercritical COR2R with spe-cial thermal properties and better flow and heat transfer characters. In this paper, heat transfer of supercritical COR2R has been investigated in square and triangle array tube-bundle of cooled system in reactor using com-putational fluid dynamics (CFD) code FLUENT, and the basic knowledge of heat transfer of supercritical COR2R and first experience of CFD simulation are obtained. The effect of mesh structures, turbulence models, as well as flow channel size is analyzed. The choice of turbulence model adopted in simulating supercritical COR2R is recommended. Comparing the effect of heat transfer with supercritical COR2R and supercritical water as cooled medium, the results show that the former was higher. The new idea is provided for choice of cooled medium and improving thermal efficiency this paper.

Safety quality of pork meat is desired by costumers, who require freshness of meat to be maintained during shelf life in storage. Among different reasons, microorganisms are the most common cause for the spoilage of chilled pork during shelf life. Pseudomonas is one of the dominant bacteria species which multiplies during storage at 4 o C. The research was focused to develop optical technology based on hyperspectral imaging technology to detect spoilage of chilled pork meat rapidly and non-invasively. Fifty four pork meat samples were bought from local market, vacuum packed and stored in refrigerator at 4 o C. Hyperspectral scattering image in the range of 400nm to 1100nm were collected from 3 different samples on each day. After the collection of hyperspectral data, the microbiological characteristics such as Total Viable Counts (TVC) and Pseudomonas spp. were determined by standard method as reference value. The scattering profiles were fitted by four-parameter Gompertz function.Based on the hyperspectral data and reference value, prediction model was developed using support vector machines (SVM) regression. Moreover, a single prediction model for pork shelf life was established by a combination of TVC and growth of Pseudomonas spp. The model thus develop yielded regression coefficient of 0.93 for pork spoilage. The result obtained in the research demonstrates that the hyperspectral technology is precise and reliable method for rapid, non-invasive, real time detection of chilled pork quality during its shelf life.

Reflectance spectral data in the range of 350 nm to 1700 nm were collected in this research to investigate the potential application of VIS/NIR to develop rapid and non-destructive optical technology for assessing the water content of fresh pork. Two prediction models were developed using partial least squares regression (PLSR) and multiple linear regression (MLR) method. The results indicated that PLSR model based on compound pre-processing methods of median smooth filter (M-filter), multiplication scatter correction (MSC), standard normalized variant (SNV) and first derivative (FD) yielded the best result. Total 57 fresh pork samples were used for developing prediction model. Cross-validation correlation coefficient and cross-validation standard deviation were 0.90 and 0.50 respectively. The correlation coefficient of 0.81 and root mean square error of 0.70 were obtained in validation set. The research results demonstrate that the visible/near-infrared combined with PLSR modeling can well detect water content in fresh pork.

Pork freshness influences the consumers' health and has become a food safety issue. The quality of pork meat can be determined in terms of its freshness, which influences the purchasing decision of the meat by consumers. The pork quality in general is recognized by its appearance and smell, which is unscientific and subjective method. As such, real-time, non-destructive, rapid and accurate detection of pork freshness is current industrial desire. A multi-channel near-infrared spectroscopy in the wave length range of 300 to 2500nm was used to detect three parameters of pork freshness namely, Total Volatile Basic-Nitrogen (TVB-N), pH value and meat color. The system mainly included hardware system and software system. The hardware system mainly included the visible and near infrared spectroscopy system, signals transmission system and transfer system. Serial signal detection, spectra collecting, data saving and processing, model predicting was planted into the software system. A controlled mechanical system was fabricated to hold the detection device and acquire the NIR spectral data. A Partial Least Square Regression (PLSR) method was used to develop prediction model of pork freshness. The research shows that the multi-channel near-infrared can be a mile-stone for development of real-time, non-destructive, rapid and accurate detection technology for accessing pork quality.