Selection Problem Research Articles

A differential evolution framework based on the fluid model for feature selection

Feature selection in machine learning is a crucial step to effectively address the issue of feature redundancy in classification problems. Numerous feature selection algorithms have been developed to minimize the number of features, reduce computational cost, and improve classification accuracy. Differential evolution algorithms have the advantage of being simple in structure, robust, fast in convergence, and frequently used to solve feature selection problems. However, it is worth noting that differential evolution algorithms are susceptible to local optimum and stagnation issues, particularly when applied to high-dimensional data. To address this issue, in this study, we propose a differential evolution framework based on the fluid model, named DEF-FM, for feature selection. DEF-FM has the capability to speed up the convergence of differential evolution algorithms and alleviate the effects of local optima. The proposed framework is validated and compared against eight popular differential evolution algorithms using 12 publicly available benchmark datasets and experimental results unequivocally demonstrate the superiority of the proposed framework.

Game Study on Morning Peak Road Congestion Problem

The morning peak road congestion problem has always been a hot issue in urban traffic management. Road congestion during the morning peak not only affects the travel efficiency of travelers, but also has a serious negative impact on the urban environment and economic development. This article starts from the perspective of game theory and focuses on the problem of road selection. This article considers government supervision and builds a game model from the two game subjects of the driver and the traffic controller. This article analyzes the relationship between the driver and the driver, the driver and the traffic controller. This paper studies the impact of traffic time, whether the driver investigates in advance, and whether the traffic commander conducts road command on the stable strategy of the driver's road choice game. The research results of this article show that drivers and drivers can achieve Nash equilibrium by choosing different roads, and drivers adopting collaborative strategies during the morning peak period can effectively alleviate road congestion. Under the condition of ensuring the cost of arranging traffic commanders for drivers and the government, drivers can investigate the road in advance and traffic commanders can direct road 2, which can effectively promote the smooth operation of the traffic system. At the same time, this article puts forward targeted policy suggestions, aiming to optimize the allocation of transportation resources, improve road traffic efficiency, reduce morning peak road congestion, and provide scientific reference and decision-making support for urban traffic management.

Universal Jurisdiction: Law out of Context

Universal jurisdiction enables the prosecution of international crimes by domestic courts in the absence of any nexus between the prosecuting state and the crime charged. While the temptation is for domestic judges to proceed with ‘business as usual’ in the conduct of such trials, difficulties in the practice of universal jurisdiction reflect the importance of developing a better understanding of the distinctive communities, interests, crimes and cultures these trials are intended to serve. The exercise of universal jurisdiction is commonly regarded as a form of domestic jurisdiction exercised pursuant to a sovereign right under international law. This article invites a re‐conceptualisation of the concept of universal jurisdiction, explaining that it is not a form of domestic jurisdiction acquired based on sovereign nexus between the crime charged and the prosecuting state. Instead, it should be recognised as a form of decentralised ‘international jurisdiction’, exercised as part of a state's contribution to the enforcement of international criminal law. This re‐conceptualisation has implications for the way in which domestic courts engage with many of the challenges facing universal jurisdiction trials, including problems of community, case selection, proof and translation.

Learning search algorithm: framework and comprehensive performance for solving optimization problems

In this study, the Learning Search Algorithm (LSA) is introduced as an innovative optimization algorithm that draws inspiration from swarm intelligence principles and mimics the social learning behavior observed in humans. The LSA algorithm optimizes the search process by integrating historical experience and real-time social information, enabling it to effectively navigate complex problem spaces. By doing so, it enhances its global development capability and provides efficient solutions to challenging optimization tasks. Additionally, the algorithm improves the collective learning capacity by incorporating teaching and active learning behaviors within the population, leading to improved local development capabilities. Furthermore, a dynamic adaptive control factor is utilized to regulate the algorithm’s global exploration and local development abilities. The proposed algorithm is rigorously evaluated using 40 benchmark test functions from IEEE CEC 2014 and CEC 2020, and compared against nine established evolutionary algorithms as well as 11 recently improved algorithms. The experimental results demonstrate the superiority of the LSA algorithm, as it achieves the top rank in the Friedman rank-sum test, highlighting its power and competitiveness. Moreover, the LSA algorithm is successfully applied to solve six real-world engineering problems and 15 UCI datasets of feature selection problems, showcasing its significant advantages and potential for practical applications in engineering problems and feature selection problems.

A Novel Hybrid Gray MCDM Model for Resilient Supplier Selection Problem

The current business climate has generated considerable uncertainty and disrupted supply chain processes. Suppliers have frequently been identified as the primary source of hazards responsible for supply chain disruptions. Using a strategic approach to supplier selection that prioritizes providers with resilience features, mitigating the risk exposure inherent in supply chains is possible. This study proposes a comprehensive gray multiple-criteria decision making (MCDM) method incorporating resilience attributes to supplier selection. To determine criteria weights, the gray PSI and gray BWM methodologies were used, and to evaluate and prioritize resilient providers, the gray MCRAT and gray COBRA methodologies were applied. According to the results obtained by the suggested methodology, the supplier that demonstrated the greatest degree of resilience was determined to be the provider categorized as SPIR 4. The sequential sequence of the SPIR numbers is as follows: SPIR 5, SPIR 1, SPIR 3, SPIR 2, and SPIR 6. The data demonstrate that the developed approach produced accurate results.

Improvement of PBFT Consensus Algorithm Based on Affinity Propagation Clustering in Intellectual Property Transaction Scenarios

In response to the problems of random selection of primary nodes, high communication complexity, and low consensus efficiency in the current consensus mechanism for intellectual property transactions, a Practical Byzantine Fault Tolerance (PBFT) consensus algorithm based on the Affinity-Propagation (AP) clustering algorithm, termed AP-PBFT, is proposed. Firstly, the election strategy of the leader node is constructed based on the reputation mechanism; the reward and punishment mechanism is designed to achieve the dynamic adjustment of the reputation value of the nodes in the PBFT consensus process, and the number of votes among the nodes is introduced to determine the node’s reputation value in collaboration with the reward and punishment mechanism to guarantee the precise ordering of the nodes. Secondly, nodes with high reputation values are selected as cluster centers to run the AP clustering algorithm, and clustering groups of knowledge property transaction nodes are constructed based on responsibility and availability. Finally, the three-stage consensus process of the PBFT consensus algorithm is optimized, and the consensus task is decomposed into two layers: the intra-consensus group and the inter-leader node group, reducing the communication complexity of transaction data in the blockchain. Experimental findings indicate a significant performance improvement of the algorithm over the PBFT consensus algorithm in communication complexity, throughput, and consensus efficiency in the simulation environment of multiple types of transactions in intellectual property transactions, including different types of large-scale transaction scenarios, such as purchases, sales, licenses, and transfers.

Performance degradation assessment of rolling bearings based on the comprehensive characteristic index and improved SVDD

Abstract Rolling bearing is one of the most critical parts of mechanical equipment, so the performance degradation assessment of rolling bearing is vital to ensure the normal operation of the whole mechanical equipment. Aiming at the problems that a single degradation characteristic can only contain limited performance degradation information of rolling bearings, a large number of redundant characteristics exist in the high-dimension characteristic set resulting in the inability to effectively mine the characteristic information of rolling bearings, and that traditional degradation assessment models are not suitable for the shortage of fault data during the actual operation of rolling bearings, a performance degradation assessment method of rolling bearings based on the comprehensive characteristic index and improved support vector data description (SVDD) is proposed in this paper. Firstly, to solve the parameter selection problem of variational mode decomposition (VMD), a parameter-adaptive VMD method based on salp swarm algorithm based on mixed strategy (MSSSA) is proposed. Secondly, to extract the performance degradation information of rolling bearings more comprehensively and fully, the comprehensive characteristic index is proposed. Then, a kernel locality preserving projection orthogonal kernel principal component analysis (KLPPOKPCA) method is proposed to reduce the dimensionality of the extracted multi-domain characteristic set of the rolling bearing. Finally, a support vector data description with negative samples (NSSVDD) is proposed and optimized by MSSSA to solve the problem that traditional degradation assessment models are not suitable for the shortage of fault data during the actual operation of rolling bearings and improve the detection performance of abnormal data. The experimental results show that the proposed method can accurately divide the performance degradation process of the rolling bearing. Moreover, the comparison with other methods further highlights the superiority of the proposed method in determining the point in time of early fault of the rolling bearing. 

Matrix‐phase material selection for shape memory polymer composites: A comparative analysis of multi‐criteria decision‐making

AbstractIn the present study, the selection of suitable shape memory polymers (SMPs) to be used as the matrix‐phase material with various (In)organic filler materials to achieve the required optimum multi‐stimuli response in shape memory polymer composites (SMPC) systems is analyzed. The selection of these materials is based on their mechanical and physical properties as well as other underlying factors such as cost, availability, shape recovery rate, and aesthetic characteristics. In this study, the entropy method was applied to estimate the weightages of the various criteria while the gray relation analysis (GRA) and the technique for order preference by similarity to ideal solution (TOPSIS). Multi‐criteria decision‐making (MCDM) approaches have been used to rank the suitable matrix‐phase polymer materials for manufacturing shape memory polymer composites (SMPC) system. A total of eight alternative SMP matrix‐phase materials based on a set of nine criteria were analyzed and ranked. The proposed methodology and the result obtained thereof have been illustrated in detail. The results obtained from TOPSIS and GRA methods have been compared to conclude the effects of the material properties on the ranking and the selection of the SMP materials. Among all the eight alternatives considered, thermoplastic polyurethane (TPU) was found to be the best material in both the MCDM methods. The material cost, resistivity, % elongation, and hydrophobicity present the most influencing properties on the SMP material selection, whereas density presents no effect on the SMP matrix material selection. The robustness of the results for the comparative analysis was verified using TOPSIS methodology to validate its reliability. It was revealed that the TPU, polycarbonate, polypropylene, and epoxy‐resin/poly(lactic acid) are the most dominant matrix‐phase SMP material alternatives when a deviation in the entropy weights of the primary evaluation criteria is applied. The novelty of this study is the exploration and application of statistical MCDM methods of engineering material selection problems based on a set of decision criteria, which can be time‐consuming and costly while using experimental analysis methods.Highlights SMPCs are applicable engineering materials thus making their research viable. SMPC systems can undergo various shape changes under external stimuli. Selection of matrix‐phase polymer is critical in achieving desired objectives. GRA and TOPSIS MCDM approaches have been applied in the selection process. TPU was found as the best material in both methods.

Adaptive soft sensor modeling of chemical processes based on an improved just‐in‐time learning and random mapping partial least squares

AbstractThe just‐in‐time learning‐based partial least squares (JIT‐PLS) has been extensively applied to adaptive soft sensor modeling of complex nonlinear processes. However, it still has the problems of unreasonable relevant samples selection and unsatisfactory local modeling. Aiming at these problems, this paper proposes an improved just‐in‐time learning‐based random mapping partial least squares (IJIT‐RMPLS), including an improved relevant samples selection strategy and a random mapping PLS (RMPLS) model. On the one hand, considering the different correlation degrees between input variables and output variable, this method applies mutual information to evaluate the importance of each input variable and designs a variable‐weighted Euclidean distance to select relevant samples for local modeling. On the other hand, in order to prompt the prediction precision of local soft sensor models, this method combines the idea of nonlinear random mapping in extreme learning machines with PLS and builds a RMPLS with multiple activation functions. Applications on a numerical example and a real chemical process show that the proposed IJIT‐RMPLS has smaller prediction error compared with traditional JIT‐PLS.

The Use of Weighted Euclidean Distance to Provide Assistance in the Selection of Safety Risk Prevention and Control Strategies for Major Railway Projects

A major railway project is a complex, giant system with multi-party participation, one characterized by complex geological conditions, long construction periods and large scale, which leads to an increased likelihood of safety risk events during construction. In order to solve the problem of scientific selection and formulation of safety risk prevention and control strategies for major railway projects, an auxiliary selection method of safety risk prevention and control strategies for major railway projects based on weighted Euclidean distance (WED) is proposed. The relevant ontology is used to conceptualize and formalize the knowledge of safety risks of major railroad projects, and combine the characteristics of major railroad projects; it refers to the prevention and control measures of historical safety risk events associated with major railroad projects, and then constructs the knowledge structure and case base around safety risks of major railroad projects and the circumstances of the case. In determining the comprehensive weights, the G1 method is used to determine the subjective weights, the anti-entropy weight method is used to determine the objective weights and game theory combines the subjective and objective weights. In comparing the array of safety risk prevention and control cases associated with major railway projects, the weighted Euclidean distance is used to calculate the similarity between these cases and the target case, which in turn assists project managers in determining the safety risk prevention and control strategies appropriate for major railway projects. This study takes Landslide No. 1 in the Tunnel A inlet planning area as an example. It utilizes the WED method to assist in selecting safety risk prevention and control strategies for major railway projects, which verifies the method’s feasibility. The proposed method enriches the method of the assisted selection of safety risk prevention and control strategies for major railway projects, makes strategy formulation more scientific, has specific reference significance for the formulation of safety risk prevention and control strategies for major railway projects, and promotes the improvement of safety risk prevention and risk control for participating units.

Developing a conceptual partner selection framework for matching public–private partnerships of rural energy internet project using an integrated fuzzy AHP approach for rural revitalization in China

The development of rural clean energy is the key to cope with the shortage of traditional energy supply in the rural revitalization strategy and improve the sustainability of rural energy supply. Under the background of digital age, the development and utilization of rural clean energy Internet has become the focus of rural economic development. The government partners of the Rural clean Energy Internet PPP project (RCEIPPPP) are the key to promoting the green and intelligent development of rural energy. In this paper, the index system of project partner selection is constructed, and the problem of government partner selection for RCEIPPPP is studied by AHP and fuzzy comprehensive evaluation. The results of this study are as follows: 1) Partners' financial ability, technical ability, management ability, performance experience, corporate reputation, cooperation ability and risk management are the influencing factors for government partner selection of rural clean energy Internet PPP projects (RCEIPPPPs); 2) Compared with other factors, financial ability, technical ability, management ability and performance experience are the four key factors that are more important in choosing partners; 3) The empirical research shows that AHP, fuzzy comprehensive evaluation and the index system constructed by this research can be applied to the practice of government partner selection for RCEIPPPPs. This study puts forward the evaluation system of government cooperation selection of energy Internet PPP projects from the theoretical level, improves the existing research methods, and makes the theoretical system in this field more complete. From the practical level, it provides scientific basis and suggestions for the government to make decisions on energy Internet PPP projects, and improves the engineering efficiency and quality of rural clean energy Internet construction. This study demonstrates the complexity of clean energy projects, the need for an integrated approach to decision-making, and the need for project managers to actively manage communication and collaboration with partners to ensure successful project implementation.

Research on Quantitative Expression of Priority for Imaging Satellite Requirements

This article conducts a quantitative expression study on the priority of imaging satellite requirements to address the issue of quickly and scientifically sorting user imaging requirements. Firstly, a priority evaluation system for imaging requirements was constructed by comprehensively considering factors such as user level, importance of requirements, observation efficiency, and urgency. Secondly, quantitative indicators were provided for the relative importance of various elements that affect the priority of satellite requirement through expert scoring, and a judgment matrix for the priority evaluation system was established. Finally, the weight coefficient vector of the evaluation system was solved using the principle of minimum square difference. A quantitative expression for imaging satellite requirement priority was constructed based on the weighted sum method. The effectiveness of the quantitative expression for imaging satellite requirement priority was verified through experimental data, effectively solving the problem of imaging requirement selection when satellite resources are insufficient.

A Two-Stage Sustainable Supplier Selection Model Considering Disruption Risk

The global spread of the pandemic has changed many aspects of life and placed the supply chain at risk of disruption. To solve the problem of supplier selection under the risk of supply chain disruption, in this paper, we propose a two-stage evaluation model to address the issue of supplier selection in the context where a pandemic requires a lockdown. First, we incorporate the lead time into the epidemic model that predicts the evolution of the pandemic to identify suppliers that have a high risk of disruption caused by the pandemic’s evolution. Second, we propose a best–worst method combined with regret theory to rank candidate suppliers. Our model provides a dynamic link between the pandemic’s evolution and supplier selection, and it allows selecting suppliers according to various criteria while avoiding supply chain disruptions due to inappropriate supplier selection. We validate the proposed model on a real case study with epidemic data from China. This paper is the first to consider the impact of lockdowns during the pandemic on supplier selection. We develop a novel MCDM model BWM-RT for supplier selection; our model can be an effective decision support approach to help decision makers better cope with the risk of supply chain disruptions.

Nix/TiO2 catalysts for enhancing the selectivity of methylcyclohexane dehydrogenation

The dehydrogenation of methylcyclohexane (MCH) is a widely used hydrogen storage technology, but nowadays, precious metal catalysts such Pt are used for this reaction and non-precious metal catalysts suffer from low selectivity and instability problems. Two catalysts, Ni20/TiO2 and Ni20/Al2O3, were prepared through the improved co-precipitation method and their dehydrogenation performance of MCH was compared. It was found that the application of TiO2 carrier could significantly reduce the acidity of the catalysts, and the nickel particles could be stably dispersed on the carriers, which could effectively improve the selectivity of the target product (toluene). In addition, the influences of different Ni contents, reaction temperatures, and weight hourly space velocity (WHSV) on the performance of the catalysts were also evaluated, and the results showed that the catalyst with 17.16% Ni content exhibited high activity with up to 86.48% conversion of MCH and 96.51% selectivity of toluene, under atmospheric pressure condition with the reaction temperature of 375°C and WHSV of 1.9 h−1, while 96.03% and only 42.64%, respectively, for the Ni20/Al2O3 catalyst. In addition, the catalyst exhibited excellent anti-coking ability, indicating that it has a promising future for industrial applications.

Decision-making for supplier selection problems based on QUALIFLEX technique using likelihood method in LIVIFS environment

The notion of linguistic interval-valued intuitionistic fuzzy set (LIVIFS) is one of the best tools in order to deal with the qualitative decision making problems. Therefore, in this paper a linguistic interval-valued intuitionistic fuzzy (LIVIF) QUALIFLEX method with a likelihood-based comparison approach is proposed. First, the notion of likelihood of fuzzy preference relation (FPRs) to compare the linguistic interval valued intuitionistic fuzzy numbers (LIVIFNs). By employing a criterion-wise preference assessment of alternatives through the comparison of likelihoods, we introduce a novel QUALIFLEX-based model. This model aims to quantify the degree of concordance in the complete preference order for effective management of decisions involving multiple criteria. We demonstrate the practicality and applicability of the proposed methods through an illustrative example, specifically focusing on the context of Supplier Selection Problems. To validate the efficacy of the proposed methodology, a comparative analysis is performed against other existing methods.

Q-Learning Guided Mutational Harris Hawk Optimizer for High-dimensional Gene Data Feature Selection

With the widespread application of high-throughput sequencing technology in recent years, the scale of high-dimensional gene sequence datasets has rapidly expanded. However, due to the high-dimensional nature of gene sequence data, researchers face the challenge of processing such complex data. One common preprocessing technique that can improve performance is feature selection, which selects the most relevant features from the original dataset, reducing its dimensionality. However, feature selection is often an NP-hard problem, and medical feature selection is mainly affected by the multiple attributes of features, which affect classification accuracy. In order to improve the generality of the algorithm, we propose RLHHO, which solves the feature selection problem of various medical gene datasets by combining Q-learning-guided mutation strategies. We also created binary RLHHO (bRLHHO) through conversion functions and evaluated its performance on 12 datasets in the UCI repository. The experimental results show that bRLHHO is superior to the original HHO in improving classification accuracy and reducing the number of selected features. When processing high-dimensional medical gene datasets with over 1000 dimensions, bRLHHO can achieve good accuracy with fewer features. In summary, compared with other algorithms, including the original Harris Hawks Optimization (HHO), our proposed improved version of HHO, RLHHO, can handle various feature selection datasets and exhibits superior performance.

Rank-Based Sequential Feature Selection for High-Dimensional Accelerated Failure Time Models with Main and Interaction Effects

High-dimensional accelerated failure time (AFT) models are commonly used regression models in survival analysis. Feature selection problem in high-dimensional AFT models is addressed, considering scenarios involving solely main effects or encompassing both main and interaction effects. A rank-based sequential feature selection (RankSFS) method is proposed, the selection consistency is established and illustrated by comparing it with existing methods through extensive numerical simulations. The results show that RankSFS achieves a higher Positive Discovery Rate (PDR) and lower False Discovery Rate (FDR). Additionally, RankSFS is applied to analyze the data on Breast Cancer Relapse. With a remarkable short computational time, RankSFS successfully identifies two crucial genes.

On identifying suitable hydrogen power plant location under [formula omitted]-spherical fuzzy hypersoft matrix structures

In recent decades, the prime objective of the energy sector has been to replace conventional energy sources with renewable energy sources such as hydrogen power, solar energy, etc. The objective of the present communication is to computationally locate the best site for the establishment of the hydrogen power plant given the various uncertainty parameters in terms of inexact and incomplete information. The uncertainty components of the information are well dealt with by the incorporation of T-spherical fuzzy hypersoft information. In accordance, we present the new concept of T-spherical fuzzy hypersoft matrix (TSFHSM) with different matrix-theoretic structured possibilities having useful computational setups. Some standard matrix operations with their algebraic properties have also been studied. Next, the structure of the proposed matrix has been utilized in presenting two new methodologies for the decision-making selection problems — one with the help of choice/weighted choice T-spherical fuzzy hypersoft matrices and the other with the value & score T-spherical fuzzy hypersoft matrices. Further, an illustrative example where the set of sites for the selection of hydrogen power plants has been computationally studied with the help of the presented novel algorithms given the available criteria. The efficacy of the proposed algorithms and their implementations have been discussed along with a comparative analysis, advantages and a characteristic comparison table.

MAGDM Model Using Single-Valued Neutrosophic Credibility Matrix Energy and Its Decision-Making Application

This paper aims to develop a MAGDM model using single-valued neutrosophic credibility matrix (SVNCM) energy in a SVNCM scenario. To do it, first, SVNCM energy and its score function are presented as a conceptual extension of existing single-valued neutrosophic matrix (SVNM) energy. Then, a MAGDM model is developed in terms of SVNCM energy and its score function in a SVNCM scenario and also its decision algorithm is provided to solve MAGDM problems with SVNCMs. Finally, the developed MAGDM model is applied in the school site selection problem as an actual example, then the comparative investigation of the decision results in the SVNM and SVNCM scenarios indicates the superiority of the developed model over existing MAGDM model.

Bluefin Trevally Optimizer (BTO): A Metaheuristic Algorithm Using Fuzzy Logic Controller for Feature Selection Problem

Metaheuristic algorithms are increasingly used for feature selection (FS) problems due to their robustness and searchability. This study presents a unique optimization technique called fuzzy bluefin trevally optimizer (BTO). BTO draws its inspiration from bluefin’s cooperative nature and sudden ambush behavior. The trevally will jump out of the water and attack its food in the air or even snag it off the water when it is sufficiently close to it. This study uses mathematical modeling to create an optimization algorithm to replicate dynamic patterns and behaviors. Since parameters play a critical role in optimization, we use the fuzzy concept for dynamic parameter adaptation in fuzzy BTO. By introducing the idea of signature and demonstrating that fuzzy BTO converges to the global optimal point, the mathematical basis for the proposed algorithm is provided. The convergence of fuzzy BTO is proved using the Markov chain property. On benchmark functions (BF), the effectiveness of the suggested strategies is evaluated and compared with other metaheuristic algorithms. Then the proposed algorithms are applied to a fuzzy FS problem. Then they are compared with traditional and recent metaheuristic algorithms on the UCI machine learning repository datasets. Finally, the statistical significance is examined using the Kruskal–Wallis test (KWT).