Metaheuristic optimization methods are iterative search processes that aim to efficiently solve complexoptimization problems. These basically find the solution space very efficiently, often without utilizing the gradi...Metaheuristic optimization methods are iterative search processes that aim to efficiently solve complexoptimization problems. These basically find the solution space very efficiently, often without utilizing the gradientinformation, and are inspired by the bio-inspired and socially motivated heuristics. Metaheuristic optimizationalgorithms are increasingly applied to complex feature selection problems in high-dimensional medical datasets.Among these, Teaching-Learning-Based optimization (TLBO) has proven effective for continuous design tasks bybalancing exploration and exploitation phases. However, its binary version (BTLBO) suffers from limited exploitationability, often converging prematurely or getting trapped in local optima, particularly when applied to discrete featureselection tasks. Previous studies reported that BTLBO yields lower classification accuracy and higher feature subsetvariance compared to other hybrid methods in benchmark tests, motivating the development of hybrid approaches.This study proposes a novel hybrid algorithm, BTLBO-Cheetah Optimizer (BTLBO-CO), which integrates the globalexploration strength of BTLBO with the local exploitation efficiency of the Cheetah Optimization (CO) algorithm. Theobjective is to enhance the feature selection process for cancer classification tasks involving high-dimensional data. Theproposed BTLBO-CO algorithm was evaluated on six benchmark cancer datasets: 11 tumors (T), Lung Cancer (LUC),Leukemia (LEU), Small Round Blue Cell Tumor or SRBCT (SR), Diffuse Large B-cell Lymphoma or DLBCL (DL), andProstate Tumor (PT).The results demonstrate superior classification accuracy across all six datasets, achieving 93.71%,96.12%, 98.13%, 97.11%, 98.44%, and 98.84%, respectively.These results validate the effectiveness of the hybrid approachin addressing diverse feature selection challenges using a Support Vector Machine (SVM) classifier.展开更多
With the development of More Electric Aircraft(MEA),the Permanent Magnet Synchronous Motor(PMSM)is widely used in the MEA field.The PMSM control system of MEA needs to consider the system reliability,and the inverter ...With the development of More Electric Aircraft(MEA),the Permanent Magnet Synchronous Motor(PMSM)is widely used in the MEA field.The PMSM control system of MEA needs to consider the system reliability,and the inverter switching frequency of the inverter is one of the impacting factors.At the same time,the control accuracy of the system also needs to be considered,and the torque ripple and flux ripple are usually considered to be its important indexes.This paper proposes a three-stage series Model Predictive Torque and Flux Control system(three-stage series MPTFC)based on fast optimal voltage vector selection to reduce switching frequency and suppress torque ripple and flux ripple.Firstly,the analytical model of the PMSM is established and the multi-stage series control method is used to reduce the switching frequency.Secondly,selectable voltage vectors are extended from 8 to 26 and a fast selection method for optimal voltage vector sectors is designed based on the hysteresis comparator,which can suppress the torque ripple and flux ripple to improve the control accuracy.Thirdly,a three-stage series control is obtained by expanding the two-stage series control using the P-Q torque decomposition theory.Finally,a model predictive torque and flux control experimental platform is built,and the feasibility and effectiveness of this method are verified through comparison experiments.展开更多
In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(...In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(CSI),which is difficult to achieve in practice.To be more practical,it is important to investigate covert communications against a warden with uncertain locations and imperfect CSI,which makes it difficult for legitimate transceivers to estimate the detection probability of the warden.First,the uncertainty caused by the unknown warden location must be removed,and the Optimal Detection Position(OPTDP)of the warden is derived which can provide the best detection performance(i.e.,the worst case for a covert communication).Then,to further avoid the impractical assumption of perfect CSI,the covert throughput is maximized using only the channel distribution information.Given this OPTDP based worst case for covert communications,the jammer selection,the jamming power,the transmission power,and the transmission rate are jointly optimized to maximize the covert throughput(OPTDP-JP).To solve this coupling problem,a Heuristic algorithm based on Maximum Distance Ratio(H-MAXDR)is proposed to provide a sub-optimal solution.First,according to the analysis of the covert throughput,the node with the maximum distance ratio(i.e.,the ratio of the distances from the jammer to the receiver and that to the warden)is selected as the friendly jammer(MAXDR).Then,the optimal transmission and jamming power can be derived,followed by the optimal transmission rate obtained via the bisection method.In numerical and simulation results,it is shown that although the location of the warden is unknown,by assuming the OPTDP of the warden,the proposed OPTDP-JP can always satisfy the covertness constraint.In addition,with an uncertain warden and imperfect CSI,the covert throughput provided by OPTDP-JP is 80%higher than the existing schemes when the covertness constraint is 0.9,showing the effectiveness of OPTDP-JP.展开更多
Feature selection(FS)is essential in machine learning(ML)and data mapping by its ability to preprocess high-dimensional data.By selecting a subset of relevant features,feature selection cuts down on the dimension of t...Feature selection(FS)is essential in machine learning(ML)and data mapping by its ability to preprocess high-dimensional data.By selecting a subset of relevant features,feature selection cuts down on the dimension of the data.It excludes irrelevant or surplus features,thus boosting the performance and efficiency of the model.Particle Swarm Optimization(PSO)boasts a streamlined algorithmic framework and exhibits rapid convergence traits.Compared with other algorithms,it incurs reduced computational expenses when tackling high-dimensional datasets.However,PSO faces challenges like inadequate convergence precision.Therefore,regarding FS problems,this paper presents a binary version enhanced PSO based on the Support Vector Machines(SVM)classifier.First,the Sand Cat Swarm Optimization(SCSO)is added to enhance the global search capability of PSO and improve the accuracy of the solution.Secondly,the Latin hypercube sampling strategy initializes populations more uniformly and helps to increase population diversity.The last is the roundup search strategy introducing the grey wolf hierarchy idea to help improve convergence speed.To verify the capability of Self-adaptive Cooperative Particle Swarm Optimization(SCPSO),the CEC2020 test suite and CEC2022 test suite are selected for experiments and applied to three engineering problems.Compared with the standard PSO algorithm,SCPSO converges faster,and the convergence accuracy is significantly improved.Moreover,SCPSO’s comprehensive performance far exceeds that of other algorithms.Six datasets from the University of California,Irvine(UCI)database were selected to evaluate SCPSO’s effectiveness in solving feature selection problems.The results indicate that SCPSO has significant potential for addressing these problems.展开更多
Addressing the complex issue of emergency resource distribution center site selection in uncertain environments, this study was conducted to comprehensively consider factors such as uncertainty parameters and the urge...Addressing the complex issue of emergency resource distribution center site selection in uncertain environments, this study was conducted to comprehensively consider factors such as uncertainty parameters and the urgency of demand at disaster-affected sites. Firstly, urgency cost, economic cost, and transportation distance cost were identified as key objectives. The study applied fuzzy theory integration to construct a triangular fuzzy multi-objective site selection decision model. Next, the defuzzification theory transformed the fuzzy decision model into a precise one. Subsequently, an improved Chaotic Quantum Multi-Objective Harris Hawks Optimization (CQ-MOHHO) algorithm was proposed to solve the model. The CQ-MOHHO algorithm was shown to rapidly produce high-quality Pareto front solutions and identify optimal site selection schemes for emergency resource distribution centers through case studies. This outcome verified the feasibility and efficacy of the site selection decision model and the CQ-MOHHO algorithm. To further assess CQ-MOHHO’s performance, Zitzler-Deb-Thiele (ZDT) test functions, commonly used in multi-objective optimization, were employed. Comparisons with Multi-Objective Harris Hawks Optimization (MOHHO), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Multi-Objective Grey Wolf Optimizer (MOGWO) using Generational Distance (GD), Hypervolume (HV), and Inverted Generational Distance (IGD) metrics showed that CQ-MOHHO achieved superior global search ability, faster convergence, and higher solution quality. The CQ-MOHHO algorithm efficiently achieved a balance between multiple objectives, providing decision-makers with satisfactory solutions and a valuable reference for researching and applying emergency site selection problems.展开更多
In recent years, particle swarm optimization (PSO) has received widespread attention in feature selection due to its simplicity and potential for global search. However, in traditional PSO, particles primarily update ...In recent years, particle swarm optimization (PSO) has received widespread attention in feature selection due to its simplicity and potential for global search. However, in traditional PSO, particles primarily update based on two extreme values: personal best and global best, which limits the diversity of information. Ideally, particles should learn from multiple advantageous particles to enhance interactivity and optimization efficiency. Accordingly, this paper proposes a PSO that simulates the evolutionary dynamics of species survival in mountain peak ecology (PEPSO) for feature selection. Based on the pyramid topology, the algorithm simulates the features of mountain peak ecology in nature and the competitive-cooperative strategies among species. According to the principles of the algorithm, the population is first adaptively divided into many subgroups based on the fitness level of particles. Then, particles within each subgroup are divided into three different types based on their evolutionary levels, employing different adaptive inertia weight rules and dynamic learning mechanisms to define distinct learning modes. Consequently, all particles play their respective roles in promoting the global optimization performance of the algorithm, similar to different species in the ecological pattern of mountain peaks. Experimental validation of the PEPSO performance was conducted on 18 public datasets. The experimental results demonstrate that the PEPSO outperforms other PSO variant-based feature selection methods and mainstream feature selection methods based on intelligent optimization algorithms in terms of overall performance in global search capability, classification accuracy, and reduction of feature space dimensions. Wilcoxon signed-rank test also confirms the excellent performance of the PEPSO.展开更多
In this paper,we investigate a distributed multi-input multi-output and orthogonal frequency division multiplexing(MIMO-OFDM) dual-functional radar-communication(DFRC) system,which enables simultaneous communication a...In this paper,we investigate a distributed multi-input multi-output and orthogonal frequency division multiplexing(MIMO-OFDM) dual-functional radar-communication(DFRC) system,which enables simultaneous communication and sensing in different subcarrier sets.To obtain the best tradeoff between communication and sensing performance,we first derive Cramer-Rao Bound(CRB) of targets in detection area,and then maximize the transmission rate by jointly optimizing the power/subcarriers allocation and the selection of radar receivers under the constraints of detection performance and total transmit power.To tackle the non-convex mixed integer programming problem,we decompose the original problem into a semidefinite programming(SDP) problem and a convex quadratic integer problem and solve them iteratively.The numerical results demonstrate the effectiveness of our proposed algorithm,as well as the performance improvement brought by optimizing radar receivers selection.展开更多
Recent advancements in computational and database technologies have led to the exponential growth of large-scale medical datasets,significantly increasing data complexity and dimensionality in medical diagnostics.Effi...Recent advancements in computational and database technologies have led to the exponential growth of large-scale medical datasets,significantly increasing data complexity and dimensionality in medical diagnostics.Efficient feature selection methods are critical for improving diagnostic accuracy,reducing computational costs,and enhancing the interpretability of predictive models.Particle Swarm Optimization(PSO),a widely used metaheuristic inspired by swarm intelligence,has shown considerable promise in feature selection tasks.However,conventional PSO often suffers from premature convergence and limited exploration capabilities,particularly in high-dimensional spaces.To overcome these limitations,this study proposes an enhanced PSO framework incorporating Orthogonal Initializa-tion and a Crossover Operator(OrPSOC).Orthogonal Initialization ensures a diverse and uniformly distributed initial particle population,substantially improving the algorithm’s exploration capability.The Crossover Operator,inspired by genetic algorithms,introduces additional diversity during the search process,effectively mitigating premature convergence and enhancing global search performance.The effectiveness of OrPSOC was rigorously evaluated on three benchmark medical datasets—Colon,Leukemia,and Prostate Tumor.Comparative analyses were conducted against traditional filter-based methods,including Fast Clustering-Based Feature Selection Technique(Fast-C),Minimum Redundancy Maximum Relevance(MinRedMaxRel),and Five-Way Joint Mutual Information(FJMI),as well as prominent metaheuristic algorithms such as standard PSO,Ant Colony Optimization(ACO),Comprehensive Learning Gravitational Search Algorithm(CLGSA),and Fuzzy-Based CLGSA(FCLGSA).Experimental results demonstrated that OrPSOC consistently outperformed these existing methods in terms of classification accuracy,computational efficiency,and result stability,achieving significant improvements even with fewer selected features.Additionally,a sensitivity analysis of the crossover parameter provided valuable insights into parameter tuning and its impact on model performance.These findings highlight the superiority and robustness of the proposed OrPSOC approach for feature selection in medical diagnostic applications and underscore its potential for broader adoption in various high-dimensional,data-driven fields.展开更多
The exponential growth of data in recent years has introduced significant challenges in managing high-dimensional datasets,particularly in industrial contexts where efficient data handling and process innovation are c...The exponential growth of data in recent years has introduced significant challenges in managing high-dimensional datasets,particularly in industrial contexts where efficient data handling and process innovation are critical.Feature selection,an essential step in data-driven process innovation,aims to identify the most relevant features to improve model interpretability,reduce complexity,and enhance predictive accuracy.To address the limitations of existing feature selection methods,this study introduces a novel wrapper-based feature selection framework leveraging the recently proposed Arctic Puffin Optimization(APO)algorithm.Specifically,we incorporate a specialized conversion mechanism to effectively adapt APO from continuous optimization to discrete,binary feature selection problems.Moreover,we introduce a fully parallelized implementation of APO in which both the search operators and fitness evaluations are executed concurrently using MATLAB’s Parallel Computing Toolbox.This parallel design significantly improves runtime efficiency and scalability,particularly for high-dimensional feature spaces.Extensive comparative experiments conducted against 14 state-of-the-art metaheuristic algorithms across 15 benchmark datasets reveal that the proposed APO-based method consistently achieves superior classification accuracy while selecting fewer features.These findings highlight the robustness and effectiveness of APO,validating its potential for advancing process innovation,economic productivity and smart city application in real-world machine learning scenarios.展开更多
Acute lymphoblastic leukemia(ALL)is characterized by overgrowth of immature lymphoid cells in the bone marrow at the expense of normal hematopoiesis.One of the most prioritized tasks is the early and correct diagnosis...Acute lymphoblastic leukemia(ALL)is characterized by overgrowth of immature lymphoid cells in the bone marrow at the expense of normal hematopoiesis.One of the most prioritized tasks is the early and correct diagnosis of this malignancy;however,manual observation of the blood smear is very time-consuming and requires labor and expertise.Transfer learning in deep neural networks is of growing importance to intricate medical tasks such as medical imaging.Our work proposes an application of a novel ensemble architecture that puts together Vision Transformer and EfficientNetV2.This approach fuses deep and spatial features to optimize discriminative power by selecting features accurately,reducing redundancy,and promoting sparsity.Besides the architecture of the ensemble,the advanced feature selection is performed by the Frog-Snake Prey-Predation Relationship Optimization(FSRO)algorithm.FSRO prioritizes the most relevant features while dynamically reducing redundant and noisy data,hence improving the efficiency and accuracy of the classification model.We have compared our method for feature selection against state-of-the-art techniques and recorded an accuracy of 94.88%,a recall of 94.38%,a precision of 96.18%,and an F1-score of 95.63%.These figures are therefore better than the classical methods for deep learning.Though our dataset,collected from four different hospitals,is non-standard and heterogeneous,making the analysis more challenging,although computationally expensive,our approach proves diagnostically superior in cancer detection.Source codes and datasets are available on GitHub.展开更多
Software defect prediction(SDP)aims to find a reliable method to predict defects in specific software projects and help software engineers allocate limited resources to release high-quality software products.Software ...Software defect prediction(SDP)aims to find a reliable method to predict defects in specific software projects and help software engineers allocate limited resources to release high-quality software products.Software defect prediction can be effectively performed using traditional features,but there are some redundant or irrelevant features in them(the presence or absence of this feature has little effect on the prediction results).These problems can be solved using feature selection.However,existing feature selection methods have shortcomings such as insignificant dimensionality reduction effect and low classification accuracy of the selected optimal feature subset.In order to reduce the impact of these shortcomings,this paper proposes a new feature selection method Cubic TraverseMa Beluga whale optimization algorithm(CTMBWO)based on the improved Beluga whale optimization algorithm(BWO).The goal of this study is to determine how well the CTMBWO can extract the features that are most important for correctly predicting software defects,improve the accuracy of fault prediction,reduce the number of the selected feature and mitigate the risk of overfitting,thereby achieving more efficient resource utilization and better distribution of test workload.The CTMBWO comprises three main stages:preprocessing the dataset,selecting relevant features,and evaluating the classification performance of the model.The novel feature selection method can effectively improve the performance of SDP.This study performs experiments on two software defect datasets(PROMISE,NASA)and shows the method’s classification performance using four detailed evaluation metrics,Accuracy,F1-score,MCC,AUC and Recall.The results indicate that the approach presented in this paper achieves outstanding classification performance on both datasets and has significant improvement over the baseline models.展开更多
Heart disease prediction is a critical issue in healthcare,where accurate early diagnosis can save lives and reduce healthcare costs.The problem is inherently complex due to the high dimensionality of medical data,irr...Heart disease prediction is a critical issue in healthcare,where accurate early diagnosis can save lives and reduce healthcare costs.The problem is inherently complex due to the high dimensionality of medical data,irrelevant or redundant features,and the variability in risk factors such as age,lifestyle,andmedical history.These challenges often lead to inefficient and less accuratemodels.Traditional predictionmethodologies face limitations in effectively handling large feature sets and optimizing classification performance,which can result in overfitting poor generalization,and high computational cost.This work proposes a novel classification model for heart disease prediction that addresses these challenges by integrating feature selection through a Genetic Algorithm(GA)with an ensemble deep learning approach optimized using the Tunicate Swarm Algorithm(TSA).GA selects the most relevant features,reducing dimensionality and improvingmodel efficiency.Theselected features are then used to train an ensemble of deep learning models,where the TSA optimizes the weight of each model in the ensemble to enhance prediction accuracy.This hybrid approach addresses key challenges in the field,such as high dimensionality,redundant features,and classification performance,by introducing an efficient feature selection mechanism and optimizing the weighting of deep learning models in the ensemble.These enhancements result in a model that achieves superior accuracy,generalization,and efficiency compared to traditional methods.The proposed model demonstrated notable advancements in both prediction accuracy and computational efficiency over traditionalmodels.Specifically,it achieved an accuracy of 97.5%,a sensitivity of 97.2%,and a specificity of 97.8%.Additionally,with a 60-40 data split and 5-fold cross-validation,the model showed a significant reduction in training time(90 s),memory consumption(950 MB),and CPU usage(80%),highlighting its effectiveness in processing large,complex medical datasets for heart disease prediction.展开更多
With the birth of Software-Defined Networking(SDN),integration of both SDN and traditional architectures becomes the development trend of computer networks.Network intrusion detection faces challenges in dealing with ...With the birth of Software-Defined Networking(SDN),integration of both SDN and traditional architectures becomes the development trend of computer networks.Network intrusion detection faces challenges in dealing with complex attacks in SDN environments,thus to address the network security issues from the viewpoint of Artificial Intelligence(AI),this paper introduces the Crayfish Optimization Algorithm(COA)to the field of intrusion detection for both SDN and traditional network architectures,and based on the characteristics of the original COA,an Improved Crayfish Optimization Algorithm(ICOA)is proposed by integrating strategies of elite reverse learning,Levy flight,crowding factor and parameter modification.The ICOA is then utilized for AI-integrated feature selection of intrusion detection for both SDN and traditional network architectures,to reduce the dimensionality of the data and improve the performance of network intrusion detection.Finally,the performance evaluation is performed by testing not only the NSL-KDD dataset and the UNSW-NB 15 dataset for traditional networks but also the InSDN dataset for SDN-based networks.Experimental results show that ICOA improves the accuracy by 0.532%and 2.928%respectively compared with GWO and COA in traditional networks.In SDN networks,the accuracy of ICOA is 0.25%and 0.3%higher than COA and PSO.These findings collectively indicate that AI-integrated feature selection based on the proposed ICOA can promote network intrusion detection for both SDN and traditional architectures.展开更多
The numerical calculation method is widely used in the evaluation of slope stability,but it cannot take the randomness and fuzziness into account that exist in rock and soil engineering objectively.The fuzzy optimizat...The numerical calculation method is widely used in the evaluation of slope stability,but it cannot take the randomness and fuzziness into account that exist in rock and soil engineering objectively.The fuzzy optimization theory is thus introduced to the evaluation of slope stability by this paper and a method of fuzzy optimal selection of similar slopes is put forward to analyze slope stability.By comparing the relative membership degrees that the evaluated object sample of slope is similar to the source samples of which the stabilities are detected clearly,the source sample with the maximal relative membership degree will be chosen as the best similar one to the object sample,and the stability of the object sample can be evaluated by that of the best similar source sample.In the process many uncertain influential factors are considered and characteristics and knowledge of the source samples are obtained.The practical calculation indicates that it can achieve good results to evaluate slope stability by using this method.展开更多
Garment online shopping has been accepted by more and more consumers in recent years. In online shopping, a buyer only chooses the garment size judged by his own experience without trying-on, so the selected garment m...Garment online shopping has been accepted by more and more consumers in recent years. In online shopping, a buyer only chooses the garment size judged by his own experience without trying-on, so the selected garment may not be the fittest one for the buyer due to the variety of body's figures. Thus, we propose a method of optimal selection of garment sizes for online shopping based on Analytic Hierarchy Process (AHP). The hierarchical structure model for optimal selection of garment sizes is structured and the fittest garment for a buyer is found by calculating the matching degrees between individual's measurements and the corresponding key-part values of ready-to-wear clothing sizes. In order to demonstrate its feasibility, we provide an example of selecting the fittest sizes of men's bottom. The result shows that the proposed method is useful in online clothing sales application.展开更多
Background:In this paper,we study the right time for an investor to stop the investment over a given investment horizon so as to obtain as close to the highest possible wealth as possible,according to a Logarithmic ut...Background:In this paper,we study the right time for an investor to stop the investment over a given investment horizon so as to obtain as close to the highest possible wealth as possible,according to a Logarithmic utility-maximization objective involving the portfolio in the drift and volatility terms.The problem is formulated as an optimal stopping problem,although it is non-standard in the sense that the maximum wealth involved is not adapted to the information generated over time.Methods:By delicate stochastic analysis,the problem is converted to a standard optimal stopping one involving adapted processes.Results:Numerical examples shed light on the efficiency of the theoretical results.Conclusion:Our investment problem,which includes the portfolio in the drift and volatility terms of the dynamic systems,makes the problem including multi-dimensional financial assets more realistic and meaningful.展开更多
To reduce the environmental impact of mechanical parts, an approach integrating structural design and material selection was studied. Adding the discrete variable of material, a hybrid optimization model was built wit...To reduce the environmental impact of mechanical parts, an approach integrating structural design and material selection was studied. Adding the discrete variable of material, a hybrid optimization model was built with the aim of minimizing environmental impact and based on an ordinary structure optimization model. An optional material set was built by combining measures of qualitative and quantitative screening, while the lifecycle environmental impact of the materials was quantified using the method of Eco-indicator 99. Two groups of structurally optimal solutions were calculated with ideal and negative-ideal materials selected respectively, and then the hybrid model was simplified by comparing the solutions. A material environmental performance index was calculated using an analytic method. By comparing this index for every material in the optional material set, the optimal material can be found and the structural solutions calculated. This method was applied to a dowel bar design process as a case study. The results show that the environmental impact of each material has a significant effect on the optimal structural solution, and it is necessary to study the integration of structural design and material selection.展开更多
Currently,e-learning is one of the most prevalent educational methods because of its need in today’s world.Virtual classrooms and web-based learning are becoming the new method of teaching remotely.The students exper...Currently,e-learning is one of the most prevalent educational methods because of its need in today’s world.Virtual classrooms and web-based learning are becoming the new method of teaching remotely.The students experience a lack of access to resources commonly the educational material.In remote loca-tions,educational institutions face significant challenges in accessing various web-based materials due to bandwidth and network infrastructure limitations.The objective of this study is to demonstrate an optimization and queueing tech-nique for allocating optimal servers and slots for users to access cloud-based e-learning applications.The proposed method provides the optimization and queue-ing algorithm for multi-server and multi-city constraints and considers where to locate the best servers.For optimal server selection,the Rider Optimization Algo-rithm(ROA)is utilized.A performance analysis based on time,memory and delay was carried out for the proposed methodology in comparison with the exist-ing techniques.The proposed Rider Optimization Algorithm is compared to Par-ticle Swarm Optimization(PSO),Genetic Algorithm(GA)and Firefly Algorithm(FFA),the proposed method is more suitable and effective because the other three algorithms drop in local optima and are only suitable for small numbers of user requests.Thus the proposed method outweighs the conventional techniques by its enhanced performance over them.展开更多
Selecting the optimal one from similar schemes is a paramount work in equipment design.In consideration of similarity of schemes and repetition of characteristic indices,the theory of set pair analysis(SPA)is proposed...Selecting the optimal one from similar schemes is a paramount work in equipment design.In consideration of similarity of schemes and repetition of characteristic indices,the theory of set pair analysis(SPA)is proposed,and then an optimal selection model is established.In order to improve the accuracy and flexibility,the model is modified by the contribution degree.At last,this model has been validated by an example,and the result demonstrates the method is feasible and valuable for practical usage.展开更多
Optimal route selection is an important function of vehicle trac flow guidance system. Its core is to determine the index weight for measuring the route merits and to determine the evaluation method for selecting rout...Optimal route selection is an important function of vehicle trac flow guidance system. Its core is to determine the index weight for measuring the route merits and to determine the evaluation method for selecting route. In this paper, subjective weighting method which relies on driver preference is used to determine the weight and the paper proposes the multi-criteria weighted decision method based on vague sets for selecting the optimal route. Examples show that, the usage of vague sets to describe route index value can provide more decision-making information for route selection.展开更多
基金funded by the Deanship of Research andGraduate Studies at King Khalid University through the Large Research Project under grant number RGP2/417/46.
文摘Metaheuristic optimization methods are iterative search processes that aim to efficiently solve complexoptimization problems. These basically find the solution space very efficiently, often without utilizing the gradientinformation, and are inspired by the bio-inspired and socially motivated heuristics. Metaheuristic optimizationalgorithms are increasingly applied to complex feature selection problems in high-dimensional medical datasets.Among these, Teaching-Learning-Based optimization (TLBO) has proven effective for continuous design tasks bybalancing exploration and exploitation phases. However, its binary version (BTLBO) suffers from limited exploitationability, often converging prematurely or getting trapped in local optima, particularly when applied to discrete featureselection tasks. Previous studies reported that BTLBO yields lower classification accuracy and higher feature subsetvariance compared to other hybrid methods in benchmark tests, motivating the development of hybrid approaches.This study proposes a novel hybrid algorithm, BTLBO-Cheetah Optimizer (BTLBO-CO), which integrates the globalexploration strength of BTLBO with the local exploitation efficiency of the Cheetah Optimization (CO) algorithm. Theobjective is to enhance the feature selection process for cancer classification tasks involving high-dimensional data. Theproposed BTLBO-CO algorithm was evaluated on six benchmark cancer datasets: 11 tumors (T), Lung Cancer (LUC),Leukemia (LEU), Small Round Blue Cell Tumor or SRBCT (SR), Diffuse Large B-cell Lymphoma or DLBCL (DL), andProstate Tumor (PT).The results demonstrate superior classification accuracy across all six datasets, achieving 93.71%,96.12%, 98.13%, 97.11%, 98.44%, and 98.84%, respectively.These results validate the effectiveness of the hybrid approachin addressing diverse feature selection challenges using a Support Vector Machine (SVM) classifier.
基金co-supported by the National Natural Science Foundation of China(No.52477063)the National Key Research and Development Program of China(No.2023YFF0719100)。
文摘With the development of More Electric Aircraft(MEA),the Permanent Magnet Synchronous Motor(PMSM)is widely used in the MEA field.The PMSM control system of MEA needs to consider the system reliability,and the inverter switching frequency of the inverter is one of the impacting factors.At the same time,the control accuracy of the system also needs to be considered,and the torque ripple and flux ripple are usually considered to be its important indexes.This paper proposes a three-stage series Model Predictive Torque and Flux Control system(three-stage series MPTFC)based on fast optimal voltage vector selection to reduce switching frequency and suppress torque ripple and flux ripple.Firstly,the analytical model of the PMSM is established and the multi-stage series control method is used to reduce the switching frequency.Secondly,selectable voltage vectors are extended from 8 to 26 and a fast selection method for optimal voltage vector sectors is designed based on the hysteresis comparator,which can suppress the torque ripple and flux ripple to improve the control accuracy.Thirdly,a three-stage series control is obtained by expanding the two-stage series control using the P-Q torque decomposition theory.Finally,a model predictive torque and flux control experimental platform is built,and the feasibility and effectiveness of this method are verified through comparison experiments.
基金supported by the CAS Project for Young Scientists in Basic Research under Grant YSBR-035Jiangsu Provincial Key Research and Development Program under Grant BE2021013-2.
文摘In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(CSI),which is difficult to achieve in practice.To be more practical,it is important to investigate covert communications against a warden with uncertain locations and imperfect CSI,which makes it difficult for legitimate transceivers to estimate the detection probability of the warden.First,the uncertainty caused by the unknown warden location must be removed,and the Optimal Detection Position(OPTDP)of the warden is derived which can provide the best detection performance(i.e.,the worst case for a covert communication).Then,to further avoid the impractical assumption of perfect CSI,the covert throughput is maximized using only the channel distribution information.Given this OPTDP based worst case for covert communications,the jammer selection,the jamming power,the transmission power,and the transmission rate are jointly optimized to maximize the covert throughput(OPTDP-JP).To solve this coupling problem,a Heuristic algorithm based on Maximum Distance Ratio(H-MAXDR)is proposed to provide a sub-optimal solution.First,according to the analysis of the covert throughput,the node with the maximum distance ratio(i.e.,the ratio of the distances from the jammer to the receiver and that to the warden)is selected as the friendly jammer(MAXDR).Then,the optimal transmission and jamming power can be derived,followed by the optimal transmission rate obtained via the bisection method.In numerical and simulation results,it is shown that although the location of the warden is unknown,by assuming the OPTDP of the warden,the proposed OPTDP-JP can always satisfy the covertness constraint.In addition,with an uncertain warden and imperfect CSI,the covert throughput provided by OPTDP-JP is 80%higher than the existing schemes when the covertness constraint is 0.9,showing the effectiveness of OPTDP-JP.
基金supported by the Fundamental Research Funds for the Central Universities of China(No.300102122105)the Natural Science Basic Research Plan in Shaanxi Province of China(2023-JC-YB-023).
文摘Feature selection(FS)is essential in machine learning(ML)and data mapping by its ability to preprocess high-dimensional data.By selecting a subset of relevant features,feature selection cuts down on the dimension of the data.It excludes irrelevant or surplus features,thus boosting the performance and efficiency of the model.Particle Swarm Optimization(PSO)boasts a streamlined algorithmic framework and exhibits rapid convergence traits.Compared with other algorithms,it incurs reduced computational expenses when tackling high-dimensional datasets.However,PSO faces challenges like inadequate convergence precision.Therefore,regarding FS problems,this paper presents a binary version enhanced PSO based on the Support Vector Machines(SVM)classifier.First,the Sand Cat Swarm Optimization(SCSO)is added to enhance the global search capability of PSO and improve the accuracy of the solution.Secondly,the Latin hypercube sampling strategy initializes populations more uniformly and helps to increase population diversity.The last is the roundup search strategy introducing the grey wolf hierarchy idea to help improve convergence speed.To verify the capability of Self-adaptive Cooperative Particle Swarm Optimization(SCPSO),the CEC2020 test suite and CEC2022 test suite are selected for experiments and applied to three engineering problems.Compared with the standard PSO algorithm,SCPSO converges faster,and the convergence accuracy is significantly improved.Moreover,SCPSO’s comprehensive performance far exceeds that of other algorithms.Six datasets from the University of California,Irvine(UCI)database were selected to evaluate SCPSO’s effectiveness in solving feature selection problems.The results indicate that SCPSO has significant potential for addressing these problems.
文摘Addressing the complex issue of emergency resource distribution center site selection in uncertain environments, this study was conducted to comprehensively consider factors such as uncertainty parameters and the urgency of demand at disaster-affected sites. Firstly, urgency cost, economic cost, and transportation distance cost were identified as key objectives. The study applied fuzzy theory integration to construct a triangular fuzzy multi-objective site selection decision model. Next, the defuzzification theory transformed the fuzzy decision model into a precise one. Subsequently, an improved Chaotic Quantum Multi-Objective Harris Hawks Optimization (CQ-MOHHO) algorithm was proposed to solve the model. The CQ-MOHHO algorithm was shown to rapidly produce high-quality Pareto front solutions and identify optimal site selection schemes for emergency resource distribution centers through case studies. This outcome verified the feasibility and efficacy of the site selection decision model and the CQ-MOHHO algorithm. To further assess CQ-MOHHO’s performance, Zitzler-Deb-Thiele (ZDT) test functions, commonly used in multi-objective optimization, were employed. Comparisons with Multi-Objective Harris Hawks Optimization (MOHHO), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Multi-Objective Grey Wolf Optimizer (MOGWO) using Generational Distance (GD), Hypervolume (HV), and Inverted Generational Distance (IGD) metrics showed that CQ-MOHHO achieved superior global search ability, faster convergence, and higher solution quality. The CQ-MOHHO algorithm efficiently achieved a balance between multiple objectives, providing decision-makers with satisfactory solutions and a valuable reference for researching and applying emergency site selection problems.
文摘In recent years, particle swarm optimization (PSO) has received widespread attention in feature selection due to its simplicity and potential for global search. However, in traditional PSO, particles primarily update based on two extreme values: personal best and global best, which limits the diversity of information. Ideally, particles should learn from multiple advantageous particles to enhance interactivity and optimization efficiency. Accordingly, this paper proposes a PSO that simulates the evolutionary dynamics of species survival in mountain peak ecology (PEPSO) for feature selection. Based on the pyramid topology, the algorithm simulates the features of mountain peak ecology in nature and the competitive-cooperative strategies among species. According to the principles of the algorithm, the population is first adaptively divided into many subgroups based on the fitness level of particles. Then, particles within each subgroup are divided into three different types based on their evolutionary levels, employing different adaptive inertia weight rules and dynamic learning mechanisms to define distinct learning modes. Consequently, all particles play their respective roles in promoting the global optimization performance of the algorithm, similar to different species in the ecological pattern of mountain peaks. Experimental validation of the PEPSO performance was conducted on 18 public datasets. The experimental results demonstrate that the PEPSO outperforms other PSO variant-based feature selection methods and mainstream feature selection methods based on intelligent optimization algorithms in terms of overall performance in global search capability, classification accuracy, and reduction of feature space dimensions. Wilcoxon signed-rank test also confirms the excellent performance of the PEPSO.
基金supported by the National Key R&D Program of China (2023YFB2905605)the National Natural Science Foundation of China (62072229)。
文摘In this paper,we investigate a distributed multi-input multi-output and orthogonal frequency division multiplexing(MIMO-OFDM) dual-functional radar-communication(DFRC) system,which enables simultaneous communication and sensing in different subcarrier sets.To obtain the best tradeoff between communication and sensing performance,we first derive Cramer-Rao Bound(CRB) of targets in detection area,and then maximize the transmission rate by jointly optimizing the power/subcarriers allocation and the selection of radar receivers under the constraints of detection performance and total transmit power.To tackle the non-convex mixed integer programming problem,we decompose the original problem into a semidefinite programming(SDP) problem and a convex quadratic integer problem and solve them iteratively.The numerical results demonstrate the effectiveness of our proposed algorithm,as well as the performance improvement brought by optimizing radar receivers selection.
文摘Recent advancements in computational and database technologies have led to the exponential growth of large-scale medical datasets,significantly increasing data complexity and dimensionality in medical diagnostics.Efficient feature selection methods are critical for improving diagnostic accuracy,reducing computational costs,and enhancing the interpretability of predictive models.Particle Swarm Optimization(PSO),a widely used metaheuristic inspired by swarm intelligence,has shown considerable promise in feature selection tasks.However,conventional PSO often suffers from premature convergence and limited exploration capabilities,particularly in high-dimensional spaces.To overcome these limitations,this study proposes an enhanced PSO framework incorporating Orthogonal Initializa-tion and a Crossover Operator(OrPSOC).Orthogonal Initialization ensures a diverse and uniformly distributed initial particle population,substantially improving the algorithm’s exploration capability.The Crossover Operator,inspired by genetic algorithms,introduces additional diversity during the search process,effectively mitigating premature convergence and enhancing global search performance.The effectiveness of OrPSOC was rigorously evaluated on three benchmark medical datasets—Colon,Leukemia,and Prostate Tumor.Comparative analyses were conducted against traditional filter-based methods,including Fast Clustering-Based Feature Selection Technique(Fast-C),Minimum Redundancy Maximum Relevance(MinRedMaxRel),and Five-Way Joint Mutual Information(FJMI),as well as prominent metaheuristic algorithms such as standard PSO,Ant Colony Optimization(ACO),Comprehensive Learning Gravitational Search Algorithm(CLGSA),and Fuzzy-Based CLGSA(FCLGSA).Experimental results demonstrated that OrPSOC consistently outperformed these existing methods in terms of classification accuracy,computational efficiency,and result stability,achieving significant improvements even with fewer selected features.Additionally,a sensitivity analysis of the crossover parameter provided valuable insights into parameter tuning and its impact on model performance.These findings highlight the superiority and robustness of the proposed OrPSOC approach for feature selection in medical diagnostic applications and underscore its potential for broader adoption in various high-dimensional,data-driven fields.
文摘The exponential growth of data in recent years has introduced significant challenges in managing high-dimensional datasets,particularly in industrial contexts where efficient data handling and process innovation are critical.Feature selection,an essential step in data-driven process innovation,aims to identify the most relevant features to improve model interpretability,reduce complexity,and enhance predictive accuracy.To address the limitations of existing feature selection methods,this study introduces a novel wrapper-based feature selection framework leveraging the recently proposed Arctic Puffin Optimization(APO)algorithm.Specifically,we incorporate a specialized conversion mechanism to effectively adapt APO from continuous optimization to discrete,binary feature selection problems.Moreover,we introduce a fully parallelized implementation of APO in which both the search operators and fitness evaluations are executed concurrently using MATLAB’s Parallel Computing Toolbox.This parallel design significantly improves runtime efficiency and scalability,particularly for high-dimensional feature spaces.Extensive comparative experiments conducted against 14 state-of-the-art metaheuristic algorithms across 15 benchmark datasets reveal that the proposed APO-based method consistently achieves superior classification accuracy while selecting fewer features.These findings highlight the robustness and effectiveness of APO,validating its potential for advancing process innovation,economic productivity and smart city application in real-world machine learning scenarios.
文摘Acute lymphoblastic leukemia(ALL)is characterized by overgrowth of immature lymphoid cells in the bone marrow at the expense of normal hematopoiesis.One of the most prioritized tasks is the early and correct diagnosis of this malignancy;however,manual observation of the blood smear is very time-consuming and requires labor and expertise.Transfer learning in deep neural networks is of growing importance to intricate medical tasks such as medical imaging.Our work proposes an application of a novel ensemble architecture that puts together Vision Transformer and EfficientNetV2.This approach fuses deep and spatial features to optimize discriminative power by selecting features accurately,reducing redundancy,and promoting sparsity.Besides the architecture of the ensemble,the advanced feature selection is performed by the Frog-Snake Prey-Predation Relationship Optimization(FSRO)algorithm.FSRO prioritizes the most relevant features while dynamically reducing redundant and noisy data,hence improving the efficiency and accuracy of the classification model.We have compared our method for feature selection against state-of-the-art techniques and recorded an accuracy of 94.88%,a recall of 94.38%,a precision of 96.18%,and an F1-score of 95.63%.These figures are therefore better than the classical methods for deep learning.Though our dataset,collected from four different hospitals,is non-standard and heterogeneous,making the analysis more challenging,although computationally expensive,our approach proves diagnostically superior in cancer detection.Source codes and datasets are available on GitHub.
文摘Software defect prediction(SDP)aims to find a reliable method to predict defects in specific software projects and help software engineers allocate limited resources to release high-quality software products.Software defect prediction can be effectively performed using traditional features,but there are some redundant or irrelevant features in them(the presence or absence of this feature has little effect on the prediction results).These problems can be solved using feature selection.However,existing feature selection methods have shortcomings such as insignificant dimensionality reduction effect and low classification accuracy of the selected optimal feature subset.In order to reduce the impact of these shortcomings,this paper proposes a new feature selection method Cubic TraverseMa Beluga whale optimization algorithm(CTMBWO)based on the improved Beluga whale optimization algorithm(BWO).The goal of this study is to determine how well the CTMBWO can extract the features that are most important for correctly predicting software defects,improve the accuracy of fault prediction,reduce the number of the selected feature and mitigate the risk of overfitting,thereby achieving more efficient resource utilization and better distribution of test workload.The CTMBWO comprises three main stages:preprocessing the dataset,selecting relevant features,and evaluating the classification performance of the model.The novel feature selection method can effectively improve the performance of SDP.This study performs experiments on two software defect datasets(PROMISE,NASA)and shows the method’s classification performance using four detailed evaluation metrics,Accuracy,F1-score,MCC,AUC and Recall.The results indicate that the approach presented in this paper achieves outstanding classification performance on both datasets and has significant improvement over the baseline models.
文摘Heart disease prediction is a critical issue in healthcare,where accurate early diagnosis can save lives and reduce healthcare costs.The problem is inherently complex due to the high dimensionality of medical data,irrelevant or redundant features,and the variability in risk factors such as age,lifestyle,andmedical history.These challenges often lead to inefficient and less accuratemodels.Traditional predictionmethodologies face limitations in effectively handling large feature sets and optimizing classification performance,which can result in overfitting poor generalization,and high computational cost.This work proposes a novel classification model for heart disease prediction that addresses these challenges by integrating feature selection through a Genetic Algorithm(GA)with an ensemble deep learning approach optimized using the Tunicate Swarm Algorithm(TSA).GA selects the most relevant features,reducing dimensionality and improvingmodel efficiency.Theselected features are then used to train an ensemble of deep learning models,where the TSA optimizes the weight of each model in the ensemble to enhance prediction accuracy.This hybrid approach addresses key challenges in the field,such as high dimensionality,redundant features,and classification performance,by introducing an efficient feature selection mechanism and optimizing the weighting of deep learning models in the ensemble.These enhancements result in a model that achieves superior accuracy,generalization,and efficiency compared to traditional methods.The proposed model demonstrated notable advancements in both prediction accuracy and computational efficiency over traditionalmodels.Specifically,it achieved an accuracy of 97.5%,a sensitivity of 97.2%,and a specificity of 97.8%.Additionally,with a 60-40 data split and 5-fold cross-validation,the model showed a significant reduction in training time(90 s),memory consumption(950 MB),and CPU usage(80%),highlighting its effectiveness in processing large,complex medical datasets for heart disease prediction.
基金supported by the National Natural Science Foundation of China under Grant 61602162the Hubei Provincial Science and Technology Plan Project under Grant 2023BCB041.
文摘With the birth of Software-Defined Networking(SDN),integration of both SDN and traditional architectures becomes the development trend of computer networks.Network intrusion detection faces challenges in dealing with complex attacks in SDN environments,thus to address the network security issues from the viewpoint of Artificial Intelligence(AI),this paper introduces the Crayfish Optimization Algorithm(COA)to the field of intrusion detection for both SDN and traditional network architectures,and based on the characteristics of the original COA,an Improved Crayfish Optimization Algorithm(ICOA)is proposed by integrating strategies of elite reverse learning,Levy flight,crowding factor and parameter modification.The ICOA is then utilized for AI-integrated feature selection of intrusion detection for both SDN and traditional network architectures,to reduce the dimensionality of the data and improve the performance of network intrusion detection.Finally,the performance evaluation is performed by testing not only the NSL-KDD dataset and the UNSW-NB 15 dataset for traditional networks but also the InSDN dataset for SDN-based networks.Experimental results show that ICOA improves the accuracy by 0.532%and 2.928%respectively compared with GWO and COA in traditional networks.In SDN networks,the accuracy of ICOA is 0.25%and 0.3%higher than COA and PSO.These findings collectively indicate that AI-integrated feature selection based on the proposed ICOA can promote network intrusion detection for both SDN and traditional architectures.
基金Sponsored by the Natural Science Foundation of Liaoning Province in China(Grant No.20022106).
文摘The numerical calculation method is widely used in the evaluation of slope stability,but it cannot take the randomness and fuzziness into account that exist in rock and soil engineering objectively.The fuzzy optimization theory is thus introduced to the evaluation of slope stability by this paper and a method of fuzzy optimal selection of similar slopes is put forward to analyze slope stability.By comparing the relative membership degrees that the evaluated object sample of slope is similar to the source samples of which the stabilities are detected clearly,the source sample with the maximal relative membership degree will be chosen as the best similar one to the object sample,and the stability of the object sample can be evaluated by that of the best similar source sample.In the process many uncertain influential factors are considered and characteristics and knowledge of the source samples are obtained.The practical calculation indicates that it can achieve good results to evaluate slope stability by using this method.
基金The Programfor New Century Excellent Talents in University from Ministry of Education of China(No.NCET-04-415)the Cultivation Fund of the Key Scientific and Technical Innovation Project from Ministry of Education of China(No.706024)International Science Cooperation Foundation of Shanghai,China(No.061307041)
文摘Garment online shopping has been accepted by more and more consumers in recent years. In online shopping, a buyer only chooses the garment size judged by his own experience without trying-on, so the selected garment may not be the fittest one for the buyer due to the variety of body's figures. Thus, we propose a method of optimal selection of garment sizes for online shopping based on Analytic Hierarchy Process (AHP). The hierarchical structure model for optimal selection of garment sizes is structured and the fittest garment for a buyer is found by calculating the matching degrees between individual's measurements and the corresponding key-part values of ready-to-wear clothing sizes. In order to demonstrate its feasibility, we provide an example of selecting the fittest sizes of men's bottom. The result shows that the proposed method is useful in online clothing sales application.
基金This work is supported by Research Grants Council of Hong Kong under grant no.519913 and 15224215National Natural Science Foundation of China(No.11571124).
文摘Background:In this paper,we study the right time for an investor to stop the investment over a given investment horizon so as to obtain as close to the highest possible wealth as possible,according to a Logarithmic utility-maximization objective involving the portfolio in the drift and volatility terms.The problem is formulated as an optimal stopping problem,although it is non-standard in the sense that the maximum wealth involved is not adapted to the information generated over time.Methods:By delicate stochastic analysis,the problem is converted to a standard optimal stopping one involving adapted processes.Results:Numerical examples shed light on the efficiency of the theoretical results.Conclusion:Our investment problem,which includes the portfolio in the drift and volatility terms of the dynamic systems,makes the problem including multi-dimensional financial assets more realistic and meaningful.
基金Project supported by the National Natural Science Foundation of China (No. 51275458)the Zhejiang Provincial Natural Science Foundation of China (No. LY12E05019)
文摘To reduce the environmental impact of mechanical parts, an approach integrating structural design and material selection was studied. Adding the discrete variable of material, a hybrid optimization model was built with the aim of minimizing environmental impact and based on an ordinary structure optimization model. An optional material set was built by combining measures of qualitative and quantitative screening, while the lifecycle environmental impact of the materials was quantified using the method of Eco-indicator 99. Two groups of structurally optimal solutions were calculated with ideal and negative-ideal materials selected respectively, and then the hybrid model was simplified by comparing the solutions. A material environmental performance index was calculated using an analytic method. By comparing this index for every material in the optional material set, the optimal material can be found and the structural solutions calculated. This method was applied to a dowel bar design process as a case study. The results show that the environmental impact of each material has a significant effect on the optimal structural solution, and it is necessary to study the integration of structural design and material selection.
文摘Currently,e-learning is one of the most prevalent educational methods because of its need in today’s world.Virtual classrooms and web-based learning are becoming the new method of teaching remotely.The students experience a lack of access to resources commonly the educational material.In remote loca-tions,educational institutions face significant challenges in accessing various web-based materials due to bandwidth and network infrastructure limitations.The objective of this study is to demonstrate an optimization and queueing tech-nique for allocating optimal servers and slots for users to access cloud-based e-learning applications.The proposed method provides the optimization and queue-ing algorithm for multi-server and multi-city constraints and considers where to locate the best servers.For optimal server selection,the Rider Optimization Algo-rithm(ROA)is utilized.A performance analysis based on time,memory and delay was carried out for the proposed methodology in comparison with the exist-ing techniques.The proposed Rider Optimization Algorithm is compared to Par-ticle Swarm Optimization(PSO),Genetic Algorithm(GA)and Firefly Algorithm(FFA),the proposed method is more suitable and effective because the other three algorithms drop in local optima and are only suitable for small numbers of user requests.Thus the proposed method outweighs the conventional techniques by its enhanced performance over them.
文摘Selecting the optimal one from similar schemes is a paramount work in equipment design.In consideration of similarity of schemes and repetition of characteristic indices,the theory of set pair analysis(SPA)is proposed,and then an optimal selection model is established.In order to improve the accuracy and flexibility,the model is modified by the contribution degree.At last,this model has been validated by an example,and the result demonstrates the method is feasible and valuable for practical usage.
基金Supported by the Provincial Government Decision-making Tender Subject(2013B318)Supported by the Educational Committee of Henan Province of China(15A520004)
文摘Optimal route selection is an important function of vehicle trac flow guidance system. Its core is to determine the index weight for measuring the route merits and to determine the evaluation method for selecting route. In this paper, subjective weighting method which relies on driver preference is used to determine the weight and the paper proposes the multi-criteria weighted decision method based on vague sets for selecting the optimal route. Examples show that, the usage of vague sets to describe route index value can provide more decision-making information for route selection.
