Adaptive Operator Research Articles

Application of Imputation Method for Compositional Data with Missing Values based on Adaptive LASSO Model: the Composition of Employment Industry in Taiyuan, China

The tripartite industry classification, which divides all economic activities into three parts, is a classification method to reflect the dynamic process of economic development and the historical trend of the change of resource allocation structure.The fact shows that the proportion of each industry has become an important symbol of the level of national economic development. The proportion of each industry is compositional data,which is a kind of complex multidimensional data used in many fields. All components in the compositional data are non-negative and carry only relative information. In practice, there could be missing values in compositional data. However, general statistical analysis methods cannot be firstly used for compositional data with missing values. The complexity of the missing value of compositional data makes traditional imputation methods no longer suitable. Thus, how to carry out effective statistical inference for compositional data with missing values attracts the attention of many scholars, recently. In this paper, we focus on the imputation problem in compositional data containing missing values, and propose an Adaptive Least Absolute Shrinkage and Selection Operator (ALASSO) imputation method to obtain a complete datasets through variable selection and parameter estimation. Then, the new method is simulated and empirically analyzed, and a comparative study with mean imputation, k-nearest neighbor imputation, and iterative regression imputation is conducted. The results show that the ALASSO imputation method has the highest accuracy for different missing rates, dimensions and correlation coefficients.

Study of Shearlet Domain Scale-Angle Adaptive Surface Wave Separation

In the seismic data processing, high energy surface wave overlaps with effective wave, which affects the quality of seismic data imaging. The traditional frequency domain method can effectively remove surface wave, but it will damage the low frequency information of seismic data. In view of this, we proposed a scale-angle adaptive surface wave separation method based on Shearlet sparse transform. The surface wave model obtained by filtering method and the effective wave can decomposed in Shearlet domain, based on the characteristic differences between surface wave and effective wave in frequency, velocity and direction. At different scales and angles, an adaptive surface wave removal operator based on L2 norm of Shearlet coefficient is developed to remove surface wave information from seismic data. The actual seismic data application results show that compared with the traditional surface wave suppression method, the proposed method can thoroughly separate surface wave and effective wave, and obtain high quality denoising seismic data.

Surprisingly Popular-Based Adaptive Memetic Algorithm for Energy-Efficient Distributed Flexible Job Shop Scheduling.

With the development of the economy, distributed manufacturing has gradually become the mainstream production mode. This work aims to solve the energy-efficient distributed flexible job shop scheduling problem (EDFJSP) while simultaneously minimizing makespan and energy consumption. Some gaps are stated following: 1) the previous works usually adopt the memetic algorithm (MA) with variable neighborhood search. However, the local search (LS) operators are inefficient due to strong randomness; 2) the confidence-based adaptive operator selection model follows the experiences of the major crowds, which ignores the efficient operators with low weight, so it can not select the really efficient operator; 3) the previous works lack of efficient strategy to save energy; and 4) the mainstream memetic framework adopts LS to all solutions, which causes the population to converge too quickly and the diversity is extremely reduced. Thus, we propose a surprisingly popular-based adaptive MA (SPAMA) to overcome the above deficiencies. The contributions are as follows: 1) four problem-based LS operators are employed to improve the convergence; 2) a surprisingly popular degree (SPD) feedback-based self-modifying operators selection model is proposed to find the efficient operators with low weight and correct crowd decision making; 3) the full active scheduling decoding is presented to reduce the energy consumption; and 4) an elite strategy is designed to balance the resources between global and LS. In order to evaluate the effectiveness of SPAMA, it is compared with state-of-the-art algorithms on Mk and DP benchmarks. The results demonstrate the superiority of SPAMA to the state-of-art algorithms for solving EDFJSP.

An effective IoT-assisted adaptive thresholding and hybrid deep learning model for rice blast fungal detection

ABSTRACT In Asian countries, rice is the major agricultural product. Hence, the initial identification of plant disorder via the IoT (Internet of Things) offers to ignore the rice from critical disorders. To increase crop production, the measurements should be considered to completely destroy the rice plant disorders by an effective system. This paper proposed an Intelligent IoT-aided deep learning model for detecting rice blast fungal along with a hybrid heuristic algorithm. The proposed work encompasses with multiple stages that are explained as follows. At first, the required paddy images are gathered from online data resources. Next, the pre-processing of the collected images is made by adaptive mean filtering and contrast enhancement. Further, the adaptive thresholding and morphological operation are adopted for leaf segmentation purposes, where the threshold value is tuned by a Fitness-based Billiards-inspired Rat Swarm Optimizer (FBRSO). Consequently, from the segmented image, the Region of Interest (ROI) is cropped. Finally, the cropped ROI is subjected to the Optimized MobileNetv2 and Multiscale Residual Attention Network (OMMRAN), where it includes MobileNetV2 and Multiscale Residual Attention Network, in which some of the hyperparameters are tuned by FBRSO approach. The performance is validated and compared with other existing approaches for detecting rice diseases effectively.

Multinomial machine learning identifies independent biomarkers by integrated metabolic analysis of acute coronary syndrome

A multi-class classification model for acute coronary syndrome (ACS) remains to be constructed based on multi-fluid metabolomics. Major confounders may exert spurious effects on the relationship between metabolism and ACS. The study aims to identify an independent biomarker panel for the multiclassification of HC, UA, and AMI by integrating serum and urinary metabolomics. We performed a liquid chromatography-tandem mass spectrometry (LC–MS/MS)-based metabolomics study on 300 serum and urine samples from 44 patients with unstable angina (UA), 77 with acute myocardial infarction (AMI), and 29 healthy controls (HC). Multinomial machine learning approaches, including multinomial adaptive least absolute shrinkage and selection operator (LASSO) regression and random forest (RF), and assessment of the confounders were applied to integrate a multi-class classification biomarker panel for HC, UA and AMI. Different metabolic landscapes were portrayed during the transition from HC to UA and then to AMI. Glycerophospholipid metabolism and arginine biosynthesis were predominant during the progression from HC to UA and then to AMI. The multiclass metabolic diagnostic model (MDM) dependent on ACS, including 2-ketobutyric acid, LysoPC(18:2(9Z,12Z)), argininosuccinic acid, and cyclic GMP, demarcated HC, UA, and AMI, providing a C-index of 0.84 (HC vs. UA), 0.98 (HC vs. AMI), and 0.89 (UA vs. AMI). The diagnostic value of MDM largely derives from the contribution of 2-ketobutyric acid, and LysoPC(18:2(9Z,12Z)) in serum. Higher 2-ketobutyric acid and cyclic GMP levels were positively correlated with ACS risk and atherosclerosis plaque burden, while LysoPC(18:2(9Z,12Z)) and argininosuccinic acid showed the reverse relationship. An independent multiclass biomarker panel for HC, UA, and AMI was constructed using the multinomial machine learning methods based on serum and urinary metabolite signatures.

An adaptive hybrid mutated differential evolution feature selection method for low and high-dimensional medical datasets

Feature selection (FS) constitutes a crucial endeavor in classification procedures, aiming to identify the minimal subset of features that maximizes classification accuracy. In the realm of combinatorial NP-hard challenges, FS assumes significance, prompting the utilization of robust metaheuristics as efficient wrapper-based FS strategies. However, the application of these wrapper metaheuristics to high-dimensional datasets, characterized by an abundance of features and a scarcity of samples, often results in a decline in effectiveness and escalated computational costs. Addressing these limitations, this study introduces the Adaptive Hybrid-Mutated Differential Evolution (A-HMDE) method, targeting the inherent drawbacks of the Differential Evolution (DE) algorithm. The A-HMDE incorporates four distinct strategies. Firstly, it integrates the mechanics of the Spider Wasp Optimization (SWO) algorithm into DE’s mutation strategies, yielding enhanced performance marked by high accuracy and swift convergence towards global optima. Secondly, adaptive mechanisms are applied to key DE parameters, amplifying the efficiency of the search process. Thirdly, an adaptive mutation operator ensures a harmonious balance between global exploration and local exploitation during optimization. Lastly, the concept of Enhanced Solution Quality (ESQ), rooted in the RUN algorithm, guides DE to elude local optima, thus heightening the accuracy of obtained solutions. The efficiency of the A-HMDE approach is assessed by employing it as a FS method across diverse datasets encompassing the UCI repository, microarray data, and skin disease image dataset. The experimental results accentuate the method’s remarkable ability to overcome challenges related to local minima and hasten the convergence process. A comprehensive comparison against contemporary cutting-edge algorithms highlights the considerable advancements achieved by the proposed method, with recorded accuracy ranging from 0.88 to 1.00.

An efficient adaptive-mutated Coati optimization algorithm for feature selection and global optimization

The feature selection (FS) problem has occupied a great interest of scientists lately since the highly dimensional datasets might have many redundant and irrelevant features. FS aims to eliminate such features and select the most important ones that affect classification performance. Metaheuristic algorithms are the best choice to solve this combinatorial problem. Recent researchers invented and adapted new algorithms, hybridized many algorithms, or enhanced existing ones by adding some operators to solve the FS problem. In our paper, we added some operators to the Coati optimization algorithm (CoatiOA). The first operator is the adaptive s-best mutation operator to enhance the balance between exploration and exploitation. The second operator is the directional mutation rule that opens the way to discover the search space thoroughly. The final enhancement is controlling the search direction toward the global best. We tested the proposed mCoatiOA algorithm in solving) in solving challenging problems from the CEC'20 test suite. mCoatiOA performance was compared with Dandelion Optimizer (DO), African vultures optimization algorithm (AVOA), Artificial gorilla troops optimizer (GTO), whale optimization algorithm (WOA), Fick's Law Algorithm (FLA), Particle swarm optimization (PSO), Harris hawks optimization (HHO), and Tunicate swarm algorithm (TSA). According to the average fitness, it can be observed that the proposed method, mCoatiOA, performs better than the other optimization algorithms on 8 test functions. It has lower average standard deviation values compared to the competitive algorithms. Wilcoxon test showed that the results obtained by mCoatiOA are significantly different from those of the other rival algorithms. mCoatiOA has been tested as a feature selection algorithm. Fifteen benchmark datasets of various types were collected from the UCI machine-learning repository. Different evaluation criteria are used to determine the effectiveness of the proposed method. The proposed mCoatiOA achieved better results in comparison with other published methods. It achieved the mean best results on 75% of the datasets.

Path Planning of an Electric Vehicle for Logistics Distribution Considering Carbon Emissions and Green Power Trading

As environmental awareness continues to grow and government policies provide incentives, electric vehicles (EVs) are becoming more widely used in logistics distribution. Considering green power trading and carbon emissions, this paper addresses the green vehicle routing problem (GVRP) and constructs an electric vehicle path model with time windows to minimize the total cost. To solve the model, a hybrid adaptive genetic algorithm (HAGA) is proposed. An improved nearest-neighbor algorithm is adopted to improve the quality of the initial population, and the adaptive crossover and mutation operators are introduced to achieve the better solution. In addition, based on the Schneider case, HAGA is used to solve the models with and without considering green power trading separately, and the results show that considering green power trading can reduce the total cost by 3.22% and carbon emissions by 23.38 kg. Finally, the experimental simulations further prove that with the increase in case size, HAGA can effectively reduce total cost. And it is beneficial for the popularization of electric vehicles in logistics distribution.

Retrieving land surface temperatures from IASI hyperspectral thermal infrared data using an AFNO-transformer model.

An adaptive Fourier neural operator (AFNO)-transformer model was developed to retrieve land surface temperature (LST) data from infrared atmospheric sounding interferometer (IASI) observations. A weight selection scheme based on linearization of the radiative transfer equation was proposed to solve the hyperspectral data channel redundancy problem. The IASI brightness temperatures and Advanced Very High Resolution Radiometer onboard MetOp (AVHRR/MetOp) LST product were selected to construct the training and test datasets. The AFNO-transformer performed effective token mixing through self-attention and effectively solved the global convolution problem in the Fourier domain, which can better learn complex nonlinear equations and achieve time-series forecasting. The root mean square error indicated that the LST in Eastern Spain and North Africa could be retrieved with an error of less than 2.5 K compared with the AVHRR/MetOp LST product. Moreover, the validation results from other time period data showed that the retrieval accuracy of this model can be less than 3 K. The proposed model provides a novel approach for hyperspectral LST retrieval.

Combining Adaptive Mathematical Morphology and Visual Reconstruction Theory for Animation Creation

Abstract This paper first discusses the use of traditional elements in animation creation and analyzes the linear narrative and artistic expression involved in its creation. Secondly, in order to carry out the image design of animation creation, the color adaptive mathematical morphology is used to carry out the image edge calculation, and the ICP algorithm in the visual reconstruction theory is introduced to carry out the point cloud alignment and fusion. Finally, the effectiveness of this paper’s method is evaluated by comparing algorithms and examples. According to the findings, the loss rate of expansion and corrosion details using the color adaptive mathematical morphology operator is 0.05% and 5.69%, respectively. The overall evaluation of the visual reconstruction presentation is 94.93% at grade C or above. This shows that using the method of this paper can accurately realize the image design of animation creation, enhance the audience’s perception of animation with a clearer view, and then promote the prosperity of the animation industry.

Associations of maternal exposure to multiple plasma metals with the risk of fetal congenital heart defects: a prospective nested case-control study

Abstract Background Congenital heart defects (CHDs) are the most common congenital anomalies with complex etiologies. There is knowledge gap with respect to the association between single and combined exposures to multiple heavy metals during pregnancy and the risk of CHDs in the fetus. Thus, we aimed to examine the association between maternal plasma heavy metal concentrations in mid-pregnancy and the risk of CHDs in the fetus. Methods A prospective nested case-control study was conducted in a cohort of 11,578 newborns. Exposure odds ratios (ORs) were compared between 164 CHD cases and 164 non-malformed controls delivered at the same hospital, individually matched by maternal age (±5 years) and parity. Concentrations of 21 metals were determined in maternal peripheral blood plasma via an ultra-performance liquid chromatography inductively coupled to mass spectrometry (ICP-MS). Single- and multiple-metal logistic regressions, the adaptive least absolute shrinkage and selection operator (LASSO) penalized regression analysis and restricted cubic spline (RCS) analysis were applied to explore the associations and dose-response relationships of plasma metals with CHD. We also applied Bayesian Kernel Machine Regression (BKMR) model to evaluate the cumulative effect of the exposure metals. Results Median (interquartile range, IQR) Vanadium (V) [0.3 (0.3-0.4) vs. 0.3 (0.3-0.4) μg/L, P = 0.027] and cadmium (Cd) [0.1 (0.0-0.1) vs. 0.1 (0.0-0.1) μg/L, P = 0.036] levels were different between the CHDs and control groups. Compared with the first quartile, the Directed acyclic graph(DAG) model-adjusted ORs (95% CI) were 3.12 (1.72-5.64) for kalium (K), 2.21 (1.23-3.94) for manganese (Mn), 0.55 (0.33-0.94) for cerium (Ce), 1.96 (1.06-3.64) for lead (Pb) in the second quartile, and ORs (95%CIs) were 1.88 (1.08-3.26) for V, 0.53 (0.30-0.93) for Cr, 1.92 (1.10-3.35) for copper (Cu), 2.09 (1.18-3.70) for Cd, 2.14 (1.24-3.70) for stannum (Sn) and 3.05 (1.69-5.49) for Pb in the third quartile. In the multi-metal model, compared to the lowest quartile, the ORs for Sn and Pb in the second quartile were 1.32 (95% CI: 0.69-2.55) and 1.94 (95% CI: 0.98-3.84), respectively, and the ORs for both metals in the third quartiles were 2.15 (95% CI: 1.16-3.98) and 3.32 (OR=0.66, 95% CI: 1.68-6.55), suggesting that Sn and Pb had relatively stable positive results with a linear dose progression. BKMR models with 2 cluster for 9 metals showed that the overall effect of metal mixture was not statistically significant at or below 35th percentile and at or above the 70th percentile, compared to their median levels. Pb was the major contributor to the combined effect. Conclusions Elevated level of plasma plumbum was associated with increased risk of CHDs. Further investigations in larger perspective cohorts are needed to confirm our findings.fig 1fig 2

Prediction nomogram for coronary artery aneurysms at one month in Kawasaki disease

BackgroundCoronary status at one month after Kawasaki disease (KD) onset had a great significance. The present study aimed to establish a prediction model for coronary artery aneurysms (CAA) at one month in children with KD.MethodsPatients with a diagnosis of KD between May 2017 and Dec 2018 were enrolled as the development cohort to build a prediction model. The model was validated by internal and external validation. Patients between Jan 2019 and Dec 2019 were enrolled as the validation cohort. The adaptive least absolute shrinkage and selection operator (LASSO) was used to select the possible predictors. Receiving operating characteristic curve (ROC), calibration plots, and decision curve analysis (DCA) were used to evaluate the performance of the model. The performance of the Son score was also assessed.ResultsLASSO regression demonstrated that age, sex, and CALs in the acute stage were predictors for CAA at one month. The area under the ROC (AUC) was 0.946 (95% confidence interval: 0.911–0.980) with a sensitivity of 92.5% and a specificity of 90.5%. The calibration curve and the DCA showed a favorable diagnostic performance. The internal and external validation proved the reliability of the prediction model. The AUC of our model and the Son score were 0.941 and 0.860, respectively (P < 0.001).ConclusionOur prediction model for CAA at one month after disease onset in KD had an excellent predictive utility.

DG-ALETSK: A High-Dimensional Fuzzy Approach With Simultaneous Feature Selection and Rule Extraction

Fuzzy or neuro-fuzzy systems have been successfully employed in many areas, but their limitation in solving high-dimensional problems remains a challenging task. On the other hand, fuzzy model based feature selection (FS) approaches have been well studied but, because of the above limitation, are rarely applied to high-dimensional data, which exactly needs feature reduction. In this paper, we design a novel adaptive Ln-Exp softmin operator as the approximator of the minimum T-norm, which is equipped in Takagi-Sugeno-Kang (TSK) fuzzy systems to make them capable of dealing with high-dimensional problems. The adaptive Ln-Exp softmin based TSK (ALETSK) model is developed. Then, we improve the gate function by introducing an enhanced scheme and propose a double groups of gates based ALETSK (DG-ALETSK) fuzzy approach to simultaneously conduct FS and rule extraction (RE), where the gate function is a function measuring the importance of the features or rules and it acts like a gate. More specifically, a group of feature gates are embedded in the antecedents of ALETSK for FS. Meanwhile, another group of rule gates are embedded in the consequents of ALETSK for RE. In one training phase, the gate parameters are trained along with the system parameters, so that the FS and RE are done together. Our proposed DG-ALETSK is effective and time-saving for FS and RE in the high-dimensional tasks, which is verified by the numerical experiments.

LAGAM: A Length-Adaptive Genetic Algorithm With Markov Blanket for High-Dimensional Feature Selection in Classification.

Feature selection (FS) is an essential technique widely applied in data mining. Recent studies have shown that evolutionary computing (EC) is very promising for FS due to its powerful search capability. However, most existing EC-based FS methods use a length-fixed encoding to represent feature subsets. This inflexible encoding turns ineffective when high-dimension data are handled, because it results in a huge search space, as well as a large amount of training time and memory overhead. In this article, we propose a length-adaptive genetic algorithm with Markov blanket (LAGAM), which adopts a length-variable individual encoding and enables individuals to evolve in their own search space. In LAGAM, features are rearranged decreasingly based on their relevance, and an adaptive length changing operator is introduced, which extends or shortens an individual to guide it to explore in a better search space. Local search based on Markov blanket (MB) is embedded to further improve individuals. Experiments are conducted on 12 high-dimensional datasets and results reveal that LAGAM performs better than existing methods. Specifically, it achieves a higher classification accuracy by using fewer features.

Sequentially coupling LBNL-method and Modelica to model and operate adaptive facades with inhomogeneous printing patterns

In this work, two U- and g-value Modelica models of a three-layer membrane construction with inhomogeneous stripe pattern were set up. Thus, a simple ISO 52022 model is compared with a detailed model with Modelica integration of Klems-Matrices by LBNL method calculation. For the U-value, both models equally predict the heat transmission. For the g-value, the detailed model shows an strong angular behavior depending on the stripe pattern, the layer distances. Compared to a reference glazing, the application of an adaptive operating strategy results in a reduction of the total energy consumption by up to 27%. Depending on the operation strategy the heating energy increases between 33-100% and cooling energy decreases by 50-80%. This is mainly due to the lower g-value with the detailed model. The actuation energy required for this can be neglected. The resulting reductions in heating and cooling energy exceed the actuation energy by several orders of magnitude.

Adaptive focus beam migration method in visco-acoustic media

Abstract Owing to the prevalent viscosity in the subsurface medium, seismic waves experience amplitude attenuation effects during their propagation in visco-acoustic media. Therefore, it is crucial to develop a method that can compensate for wavelet amplitude attenuation and enhance imaging quality. In this paper, we derive an expression for corrected ray complex travel time by introducing the quality factor Q. Additionally, we modify the classical Gaussian beam propagation operator to an adaptive focus type propagation operator. Our research presents an adaptive focused beam migration imaging method specifically designed for viscous acoustic media, incorporating a combination of traditional Gaussian beam migration imaging methods. In comparison to traditional migration methods, the proposed approach achieves energy focusing along the phase axis and significantly improves imaging quality. The validity and effectiveness of our method are confirmed through the obtained imaging results.

Inverse model and adaptive neighborhood search based cooperative optimizer for energy-efficient distributed flexible job shop scheduling

Solving the energy-efficient distributed flexible job shop scheduling problem (EEDFJSP) obtains increased attention. However, most previous studies barely considered the large-scale nature of the decision variables of EEDFJSP. In this paper, the large-scale EEDFJSP with two minimized objectives of makespan and total energy consumption (TEC) is studied. To efficiently deal with this problem, an inverse model and adaptive neighborhood search based cooperative optimizer is proposed. First, the inverse model is applied to the job shop scheduling problem. Then, the inverse model and adaptive local search operators cooperate search is designed to obtain offspring. Furthermore, an adaptive strategy for local search operators is developed. Finally, it is compared with other multi-objective optimization algorithms to confirm the effectiveness of the proposed algorithm, including MOEA/D, NSGA-II, IM-MOEA/D, HMMA, HSLFA, and SPAMA. Experimental results demonstrate the superior performance in solving EEDFJSP compared to these six state-of-the-art multi-objective optimization algorithms.

A Niche Adaptive Elite Evolutionary Algorithm for the Clustering Optimization of Intelligent Unmanned Agricultural Unmanned Aerial Vehicle Swarm Collaboration Networks

Nowadays, the intelligent unmanned agricultural unmanned aerial vehicle (UAV) swarm collaboration network (AUSCN) has fully demonstrated its advantages in agricultural monitoring and management. By using an AUSCN, multi-machine cooperation can be realized to expand the detection range, and more complex tasks can be completed without human participation, so as to improve work efficiency and reduce the consumption of manpower and material resources. In AUSCNs, clustering is a key method to lower energy consumption. However, there is a challenge to select cluster heads in AUSCNs because of the limitation of transmission distances and the complexity of network topological structures. In addition, this problem has been confirmed as NP-hard. In this paper, a new niche adaptive elite evolutionary algorithm (NAEEA) is proposed to solve this problem. NAEEAs can search within various complicated stochastic situations at high speeds with characterized high precision and fast convergence. This algorithm integrates the merits of elite selection and adaptive adjusting to achieve high performance. In NAEEAs, a new adaptive operator is designed to speed up the convergence rate, while a novel elite operator is proposed to avoid local optima and raise the exploration ability. Furthermore, a new niche operator is also proposed to increase the population diversity. The simulation results show that, compared with an evolutionary algorithm (EA), a simulated annealing algorithm (SA) and a leapfrog algorithm (SFLA), clustering energy consumption based on an NAEEA is significantly reduced, and the network energy consumption of the AUSCN is up to 21.43%, 25.00% and 25.76% lower than the other three algorithms, respectively.

Machine learning interfaces for modular modelling and operation-based design optimization of solar thermal systems in process industry

The trend of introducing solar thermal systems (STSs) in process industries has resulted in a new energy paradigm– an interactive platform where there are economic benefits and motivations to address sustainable development. On the other hand, this paradigm has also introduced fluctuations and uncertainties not previously seen on the energy system, and is challenging the industry. Industrial STSs are composed of large number of interacting, nonlinear and uncertain subsystems and, therefore are complex systems. This complexity of heterogenous, stochastic, and dynamic behavior make designing of industrial STSs as well as evaluation of their management strategies challenging tasks for the existing tools. In an attempt to support integration decisions, always inefficient and insufficient, machine learning (ML) is leveraged in this research. Following the ML approach, some important findings were made. Firstly, it permits for different releases of the same/different module, which can be easily embedded, coupled exogenously or run in parallel. Thus, the operation-based design approach, both for process and supply level integration of STSs, were scalable and tractable. Distinguished from past work, the approach also allowed testing of an improved STS sizing scheme, while at the same time, enabling optimization of its control concept for adaptive and flexible operation. In the considered case study, inclusion of time-varying heat demand, process temperature and solar irradiation in STS design reduces over-dimensioning by 10–30%. Furthermore, optimizing the control concept of the overall system led to improved energy efficiency of solar field and storage respectively by 23.7% and 8.3% over the state-of-the-art methods.

A novel multi-agent genetic algorithm for limited-view computed tomography

Computed tomography (CT) is a non-invasive testing technique to generate the internal image of any object. It has various applications belonging to medical imaging and other engineering applications. The present article proposes a novel multiagent genetic algorithm based on hill-climbing for limited-view computed tomography (MAGAH-CT). The MAGAH combines a multi-agent system based on a genetic algorithm (MAGA) and a hill-climbing approach. The MAGAH uses multiple operators to refine the agent population during evolution. These operators are neighborhood-based competition, hybrid crossover, adaptive mutation, and self-learning. MAGAH-CT explores better solutions in neighborhood-based competition and cooperation operators. The self-learning operator learns through the agent’s population generated by adaptive mutation and hybrid crossover operator, ensuring the results’ continuity. The proposed algorithm has been tested on a Shepp-Logan head phantom with limited view projection data. The algorithm can accurately reconstruct the low-noise CT image using limited data constraints. Experimental results reveal that the presented MAGAH-CT algorithm is efficient and computationally reasonable. The presented algorithm is suitable for low-noise CT reconstruction and out-performs other limited-view CT reconstruction algorithms.