Selection Problem Research Articles

Hybrid coati–grey wolf optimization with application to tuning linear quadratic regulator controller of active suspension systems

Vehicle suspension systems have become increasingly crucial for both driving safety and comfort. Active suspension systems can dynamically adjust suspension characteristics in real-time by introducing force into the system. Designing a controller for the real-time adjustment of the control force in active suspension systems is essential to meet challenging control objectives, including body acceleration, suspension deflection, and tire deflection. This article proposes a hybrid optimization approach named Coati–Grey Wolf Optimization (COAGWO), which combines the strengths of the coati optimization algorithm and grey wolf optimization to tune the gains of linear quadratic control applied to vehicle suspension systems. The COAGWO algorithm incorporates a unique strategy inspired by the Coati Optimization Algorithm, allowing wolves to climb trees. This enhancement significantly improves the wolves’ ability to explore the global search space and reduces the likelihood of being trapped in local optima. Initially, we conduct extensive experiments using a suite of challenging optimization problems from the CEC2019 benchmark to evaluate the effectiveness of the COAGWO algorithm. The effectiveness of COAGWO is compared against several state-of-the-art algorithms, including grey wolf, coati, aquila-grey wolf, whale, reptile search, tunicate swarm, and seagull optimization algorithms. The experimental results demonstrate that COAGWO consistently outperforms these algorithms in terms of solution quality and convergence speed. For the optimal weight selection problem of linear quadratic control applied to the control of vehicle suspension systems, the excellent performance of the proposed method is illustrated through comparative simulation studies under various road disturbance conditions. The results indicate that the COAGWO algorithm achieves a more efficient active suspension system compared to competitor algorithms by reducing the overall acceleration of the driver’s body, thereby enhancing ride comfort.

Variable selection for both outcomes and predictors: sparse multivariate principal covariates regression

AbstractDatasets comprised of large sets of both predictor and outcome variables are becoming more widely used in research. In addition to the well-known problems of model complexity and predictor variable selection, predictive modelling with such large data also presents a relatively novel and under-studied challenge of outcome variable selection. Certain outcome variables in the data may not be adequately predicted by the given sets of predictors. In this paper, we propose the method of Sparse Multivariate Principal Covariates Regression that addresses these issues altogether by expanding the Principal Covariates Regression model to incorporate sparsity penalties on both of predictor and outcome variables. Our method is one of the first methods that perform variable selection for both predictors and outcomes simultaneously. Moreover, by relying on summary variables that explain the variance in both predictor and outcome variables, the method offers a sparse and succinct model representation of the data. In a simulation study, the method performed better than methods with similar aims such as sparse Partial Least Squares at prediction of the outcome variables and recovery of the population parameters. Lastly, we administered the method on an empirical dataset to illustrate its application in practice.

Neuroadaptive‐based fixed‐time control for robotic manipulators with uniform prescribed performance under unknown disturbance

AbstractAchieving faster convergence, smaller transient overshoots, and higher steady‐state tracking accuracy is essential to improve the efficiency, robustness, and applicability of robotic manipulators. This article introduces an innovative adaptive fixed‐time uniform prescribed performance controller for the manipulator facing model uncertainties and unknown disturbances. Initially, by designing a modified prescribed performance function inspired by variable superposition, this study redefines the unified prescribed performance control problem into a simplified parameter selection problem. This approach allows for the incorporation of varied performance metrics within a singular control scheme, addressing both transient and steady‐state performances concurrently without shifting control frameworks. Then, to alleviate computational demands, an adaptive neural network employing a single‐parameter weight update technique compensates for uncertainties of the manipulator dynamic model. Additionally, a disturbance observer is designed to mitigate the impact of non‐parametric disturbances. Moreover, integrating fixed‐time theory with the Lyapunov stability analysis method guarantees the convergence of all error signals to a near‐zero compact neighborhood at a fixed time. Finally, the advantages and comprehensive performance of the proposed method are confirmed by numerical simulations and real‐world experiments.

An improved mountain gazelle optimizer based on chaotic map and spiral disturbance for medical feature selection.

Feature selection is an important solution for dealing with high-dimensional data in the fields of machine learning and data mining. In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. First, the gazelle population is initialized using iterative chaotic map with infinite collapses (ICMIC) mapping, which increases the diversity of the population. Second, a nonlinear control factor is introduced to balance the exploration and exploitation components of the algorithm. Individuals in the population are perturbed using a spiral perturbation mechanism to enhance the local search capability of the algorithm. Finally, a neighborhood search strategy is used for the optimal individuals to enhance the exploitation and convergence capabilities of the algorithm. The superior ability of the IMGO algorithm to solve continuous problems is demonstrated on 23 benchmark datasets. Then, BIMGO is evaluated on 16 medical datasets of different dimensions and compared with 8 well-known metaheuristic algorithms. The experimental results indicate that BIMGO outperforms the competing algorithms in terms of the fitness value, number of selected features and sensitivity. In addition, the statistical results of the experiments demonstrate the significantly superior ability of BIMGO to select the most effective features in medical datasets.

Application of Cyrene solvent for the fabrication of polymethyl methacrylate, polyethyl methacrylate and composite films containing hydroxyapatite and diamonds

AbstractCyrene is a new biodegradable solvent, which represents a green alternative to many traditional toxic solvents for advanced polymers. Therefore, this investigation is motivated by interest in applications of Cyrene for polymer processing. The feasibility of solubilization of polymethyl methacrylate (PMMA) and polyethyl methacrylate (PEMA) in Cyrene is demonstrated. The ability to form relatively concentrated solutions of the high molecular mass polymers is an important factor for the formation of PMMA and PEMA films by a dip coating method. It was found that the polymer coatings provide corrosion protection of stainless steel in 30 g L−1 NaCl solutions. Another important finding is that the use of Cyrene facilitates the fabrication of stable suspensions of hydroxyapatite (HAp), microdiamond, and nanodiamond particles. Therefore, the problems of the dispersant selection for dispersion of chemically inert diamond and development of biocompatible dispersants are avoided using Cyrene. For the fabrication of composite films, HAp nanorods with reduced particle size are obtained using rutin as a chelating capping agent. Composite films containing HAp, microdiamond and nanodiamond in the PMMA or PEMA matrix are obtained. The composite films are promising for biomedical applications. It was found that the film morphology, composition and microstructure are influenced by the solvent–particle–polymer interactions and processing conditions.

Cooperative coati optimization algorithm with transfer functions for feature selection and knapsack problems

Cooperative coati optimization algorithm with transfer functions for feature selection and knapsack problems

An algorithmic multiple attribute decision-making context to model uncertainty associated with hospital site selection problem using complex sv-neutrosophic soft information

ABSTRACT Decision-making approaches are often used in uncertain environments by people who must make difficult judgments in daily life, including elements of varied qualities and costs. These methods assist decision-makers in managing ambiguity and uncertainty, allowing for more informed and risk-reduced decisions. This research introduces an advanced framework called a complex single-valued neutrosophic soft set (csvNSS) to address uncertainties inherent in decision-making. The csvNSS framework is capable of managing information periodicity by introducing two components: amplitude and phase. The first deals with fuzzy membership, while the second manages periodicity within a complex plane. Some rudiments of csvNSS like properties, set operations and aggregations, are investigated. To make these ideas practically applicable in choosing an appropriate location for the hospital, an algorithm for handling csvNSS is proposed. An enhanced strategy is validated through the use of a specific example that takes site selection for hospital into account. The outcome demonstrates the efficacy of the suggested strategy. The method can be used in other domains where selection issues arise.

Bad governance in Australia and how to mitigate it

AbstractWe argue that new public management (NPM) and other processes have led to adverse selection and agency rent problems within the political elite in Australia. The politicisation of the public service and the rise of careerist politicians have led to the de‐separation of what was once two distinct career paths within the political elite. This has changed the decision premise of the advisory system for policy formation and implementation, leading to worse public policy, even policy disasters. We recommend that some of the principles of the Westminster form of government be updated and re‐instituted for political elites within the structures of the Australian government. We canvass four reforms: return to merit appointments and promotion for public servants; a return to tenure for senior public servants; an enhanced push for descriptive representation within the career public service; and a formal role for staffers with centralised appointments, inability to instruct public servants, and restrictions on political and policy roles once leaving public service. These are designed to end the distrust, perceived corruption, and poor governance in the Australian government.Points for practitioners Having separate career paths for non‐elected policy elites and elected elites is vital for information transmission and good governance. Each should operate with separate decision premises and work together in creative tension. NPM practices have weakened the separate career paths of public servants and politicians, leading to adverse selection and agency rent for both. The Westminster system has a logic that has been diluted or weakened within the Australian government, leading to poorer policy and policy disasters. The paper presents four areas for reform.

Research on Load Decomposition and Optimization of Intelligent Elderly Care Service Based on Heuristic and Event Detection Algorithm

Against the backdrop of the digital age, the openness, equality and interaction of the Internet economy have injected new vitality into China’s traditional industries. The application of big data technology, especially in information integration and analysis, has become a key force in promoting the sustainable and healthy development of the national economy. This study focuses on the “Internet +” environment, discusses the impact of the aging problem of community workers on home care services, and proposes an optimization scheme based on a heuristic algorithm. The heuristic algorithm, inspired by the foraging behavior of ants in nature, optimizes the route selection problem by simulating an ant colony to choose the path with a high concentration of pheromones and shows outstanding application potential in the field of home care. The accuracy of the event detection algorithm is directly related to the performance of the load decomposition algorithm, and the change point detection algorithm can effectively identify the change point of the probability distribution in the time series data, which provides important input data for unsupervised clustering. Advanced computer theory, including the Hidden Markov model (HMM) and swarm intelligence optimization algorithm, is used in this research. By comparing different swarm intelligence algorithms, we find that the standard Gray Wolf optimization (SGWO) model is better than the basic Gray Wolf optimization (BGWO) algorithm and the improved Gray Wolf optimization (DGWO) algorithm in terms of stability and output results. The SGWO model significantly improves the efficiency of the load decomposition algorithm, which has been verified in the application of the smart elderly care service platform. The platform not only supports the operation of related technologies and information products but also realizes the seamless integration of information among various subjects of elderly care services. In addition, the factor hidden in the Markov model that can be selectively activated effectively monitors equipment status in the Internet of Things environment, provides real-time monitoring of user consumption behavior and fault information and further enhances the quality and efficiency of smart elderly care services.

A Conceptual Approach of an Integrated Multi Criteria Decision Making Techniques and Deep Learning for Construction Project Managers Selection Problem

A Conceptual Approach of an Integrated Multi Criteria Decision Making Techniques and Deep Learning for Construction Project Managers Selection Problem

Instrumental Variable Method for Regularized Estimation in Generalized Linear Measurement Error Models

Regularized regression methods have attracted much attention in the literature, mainly due to its application in high-dimensional variable selection problems. Most existing regularization methods assume that the predictors are directly observed and precisely measured. It is well known that in a low-dimensional regression model if some covariates are measured with error, then the naive estimators that ignore the measurement error are biased and inconsistent. However, the impact of measurement error in regularized estimation procedures is not clear. For example, it is known that the ordinary least squares estimate of the regression coefficient in a linear model is attenuated towards zero and, on the other hand, the variance of the observed surrogate predictor is inflated. Therefore, it is unclear how the interaction of these two factors affects the selection outcome. To correct for the measurement error effects, some researchers assume that the measurement error covariance matrix is known or can be estimated using external data. In this paper, we propose the regularized instrumental variable method for generalized linear measurement error models. We show that the proposed approach yields a consistent variable selection procedure and root-n consistent parameter estimators. Extensive finite sample simulation studies show that the proposed method performs satisfactorily in both linear and generalized linear models. A real data example is provided to further demonstrate the usage of the method.

Monitoring and Prediction of Ground Surface Settlement in Kunming Urban Area by Building GWO-LSTM Model Based on TS-InSAR

Abstract: Long-term series monitoring of ground surface settlement by remote sensing has become an effective method. Based on TS-InSAR (time series Interferometric Synthetic Aperture Radar) interferometry, this paper proposes a new model based on the Grey Wolf Optimizer (GWO) and Long Short-Term Memory (LSTM) to monitor and predict the ground surface settlement in Kunming City. The results show that the MAPE (mean absolute percentage error), RMSE (root mean square error), and MAE (mean absolute error) of GWO-LSTM are significantly reduced. R2 (goodness of fit) in the six sub-study areas of Kunming City is improved in comparison to the LSTM model. The problem of manual parameter selection in the LSTM model is solved by the GWO algorithm to select parameters automatically. This approach not only significantly reduces the model’s training time but also identifies the most suitable network parameters. This can bring the best performance. Based on TS-InSAR data, the prediction of urban ground surface settlement by the GWO-LSTM model has good accuracy and robustness, which offers a scientific foundation for monitoring and issuing early warnings about urban land disasters.

Marginalized LASSO in the low-dimensional difference-based partially linear model for variable selection

The difference-based partially linear model is an appropriate regression model when both linear and nonlinear predictors are present in the data. However, when we want to optimize the weights using the difference-based method, the problem of variable selection can be difficult since low-variance predictors present a challenge. Therefore, this study aims to establish a novel methodology based on marginal theory to tackle such mixed relationships successfully, emphasizing variable selection in low dimensions. We suggest using a marginalized LASSO estimator with a penalty term that is not as severe and related to the difference order. As part of our numerical analysis of small sample performance, we undertake comprehensive simulation experiments to numerically demonstrate the strength of our proposed technique in estimation and prediction compared to the LASSO under a low-dimensional setup. This is done so that we can numerically demonstrate the strength of our proposed method in estimation and prediction. The bootstrapped method is utilized to evaluate how well our proposed prediction method performs when examining the King House dataset.

Marginalized LASSO in the low-dimensional difference-based partially linear model for variable selection

The difference-based partially linear model is an appropriate regression model when both linear and nonlinear predictors are present in the data. However, when we want to optimize the weights using the difference-based method, the problem of variable selection can be difficult since low-variance predictors present a challenge. Therefore, this study aims to establish a novel methodology based on marginal theory to tackle such mixed relationships successfully, emphasizing variable selection in low dimensions. We suggest using a marginalized LASSO estimator with a penalty term that is not as severe and related to the difference order. As part of our numerical analysis of small sample performance, we undertake comprehensive simulation experiments to numerically demonstrate the strength of our proposed technique in estimation and prediction compared to the LASSO under a low-dimensional setup. This is done so that we can numerically demonstrate the strength of our proposed method in estimation and prediction. The bootstrapped method is utilized to evaluate how well our proposed prediction method performs when examining the King House dataset.

Research on intelligent nesting algorithm for irregular ship parts based on no-fit-polygon

In order to improve the utilization ratio of steel plates for parts nesting in ship manufacturing, this paper proposes a new intelligent nesting algorithm for irregular parts based on NFP, which can effectively solve the three difficult problems of selection, locating and collision for irregular parts nesting, and further improve the utilization rate of steel plates. The new method directly uses irregular parts as nested objects, establishes the NFP generating algorithm based on the trajectory and Minkowski method to solve the collision problem, proposes a hybrid strategy of the TOPOS principle and BL principle to solve the locating problem, and builds ISAGA to optimize the sequence and rotation angle of the irregular part to solve the selection problem. Finally, the feasibility and effectiveness of the new method are verified by comparing with the existing nesting softwares of Nest Lib and Sigma Nest based on 10 standard test cases of irregular parts in the ESICUP, the experimental results show that the plate utilization of the proposed new method increased on average by 9.76 % and 13.56 %. And compared with the actual packing results of irregular parts in a shipyard, the plate utilization rate of the new method increased by 5.04 % on average.The new method will play an important role in further improving the utilization rate of steel plate in the shipbuilding industry.

A flexible combinatorial auction bidding language for supplier selection and order allocation in a supply chain with price sensitive demand

This study addresses the multi-item multi-sourcing supplier selection and order allocation problem in a Combinatorial Auction (CA) framework. First, we propose a new flexible procurement CA bidding language, then prove that it can convey the same information as existing bidding languages while allowing more efficient auction outcomes and reduced computational complexity. Suppliers submit combinatorial bids to a single manufacturer. Each bid carries information about the supplier’s cost structure and potential discounts. The manufacturer purchases the items and assembles one or more product types that experience price-sensitive demand rates by end consumers. We use the realistic logit function to represent the price-sensitive demand rates of finished products. A Mixed Integer Non-Linear Programming (MINLP) model is developed to help the manufacturer make optimal supplier selection, order allocation, and pricing decisions under the proposed bidding language. The MINLP model considers purchasing, transportation, ordering, administrative, holding, and manufacturing costs. These considerations make the results of this study realistic with the potential to be used in practical applications. Also, we derive a set of necessary conditions that must exist in at least one optimal solution to the problem. These conditions can be utilized to design efficient solution mechanisms for this difficult problem in the future. Finally, an extensive numerical analysis is performed to illustrate the bidding language and the key results.

A Convex combination of improved Fletcher-Reeves and Rivaie-Mustafa-Ismail-Leong conjugate gradient methods for unconstrained optimization problems and applications

Conjugate gradient methods are probably the most used methods in solving large scale unconstrained optimization problems. They have become popular because of their simplicity and low memory requirements. In this paper, we propose a hybrid conjugate gradient method based on the improved Fletcher-Reeves (IFR) and Rivaie-Mustafa-Ismail-Leong+ (RMIL+) methods and establish its global convergence under the strong Wolfe line search conditions. The new conjugate gradient direction satisfies the sufficient descent condition. The method is then compared to other methods in the literature and numerical experiments show that it is competent when solving large scale unconstrained optimization problems. Furthermore, the method is applied to solve a problem in portfolio selection.

Determinants of household food insecurity in Tanzania: A Heckman approach

Household food insecurity is a predominant issue in Tanzania, particularly affecting rural areas where poverty rates are higher. This problem can lead to poor nutritional quality and hinder mental, social, and economic development. The study utilized data from the 2017/18 Tanzania Household Budget Survey to analyse the determinants of food insecurity. Employing the Heckman approach to address the sample selection problem, the study found that household characteristics such as sex of the household head, location, education level, access to credit, household size, marital status, and income significantly reduce the problem of food insecurity among insecure groups. Male-headed, urban location, credit-accessible households and married heads were less food insecure compared to female-headed, rural location and households without access to credit, respectively. In addition, higher household income and education level, as well as smaller household size, positively impacted food expenditure among households. The findings suggest the need for improved credit services, formal education promotion, and targeted food programs, particularly in rural areas, to address household food insecurity effectively in Tanzania.

Optimized dispersion Higuchi fractal dimension and its refined composite multi-scale version for signal analysis

Higuchi fractal dimension (HFD), as a classic nonlinear dynamic metric, which is commonly used to detect signal dynamic changes. However, it is difficult for HFD to process signal outliers. To address this issue, dispersion HFD (DHFD) is proposed, which improves the signal complexity representation ability of HFD by introducing the normal cumulative distribution function and round function in dispersion entropy. Nevertheless, the parameter selection of DHFD can affect the complexity value. Therefore, an optimized dispersion HFD (ODHFD) is proposed, which solves the threshold selection problem of DHFD and can more effectively reflect the complexity of the signal. In addition, an optimized refined composite DHFD (ORCMDHFD) has been proposed, which can more comprehensively reflect the complexity information for the signal at multiple scales. The simulation experiment results show that DHFD has a smaller standard deviation than HFD when calculating white noise signal complexity, and DHFD have the least dependence on signal length compared to other metrics, as well as RCMDHFD has the best separability for simulated noise signals. Actual experiments have shown that ODHFD and ORCMDHFD is superior to other entropy and fractal dimension metrics in distinguishing ship radiated noise and mechanical fault signals, and has broad application prospects in the field of signal analysis.

Vertical Switching Algorithm for Unmanned Aerial Vehicle in Power Grid Heterogeneous Communication Networks

The rapid development of wireless network technology has led to the coexistence of various heterogeneous wireless networks (HWNs). To ensure that users enjoy normal and diversified services, research on vertical switching technology has become an inevitable trend. However, most current vertical switching algorithms only consider static situations or single services, which are not suitable for power grid scenarios. This paper studies the vertical switching problem of wireless heterogeneous networks for unmanned aerial vehicles (UAVs) performing inspection tasks in power grid scenarios. In this model, a UAV for power grid inspection needs to plan its flight trajectory, avoid obstacles, and find the optimal trajectory to reach each inspection point. Throughout the UAV inspection process, we must ensure the quality of communication services for the UAV. The UAV dynamically selects different networks for access at different locations, presenting a dynamic network selection and vertical switching problem. This paper proposes a method that combines trajectory planning and network selection, which first utilizes the A-star algorithm to obtain suitable trajectories, and then evaluates and judges networks based on the Fuzzy Analytic Hierarchy Process (FAHP) to determine the most appropriate network. It is worth noting that this paper considers three service requirements and seven network attributes under three types of heterogeneous wireless networks. Numerical results show that this method can better meet the requirements of UAV inspection tasks and reduce the number of switches, thus addressing the issue of terminal vertical switches in power grid scenarios.