The trade-off between users’ fairness and network throughput may be unacceptable in a multi-rate 802.11 WLAN environment. In this paper, we will design a new intuitive simplified mathematical model called simplified ...The trade-off between users’ fairness and network throughput may be unacceptable in a multi-rate 802.11 WLAN environment. In this paper, we will design a new intuitive simplified mathematical model called simplified coefficient of variation (SCV) to closely reflect our topic. Through controlling the power of Access Points, SCV can optimize and improve the performance. Since our topic is a NP-hard problem, we use Ant Colony Algorithm to solve our model in a practical scenario. The simulation shows excellent results indicating that our model is efficient and superior to an existing method. Also we use software SAS to further reveal the relationships among the three indicators to illustrate the essence of our approach and an existing algorithm.展开更多
Proteus mirabilis is abundant in soil and water,and although it is part of the normal human intestinal flora(along with Klebsiella species and Escherichia coli),it is known to cause serious infections in humans,with a...Proteus mirabilis is abundant in soil and water,and although it is part of the normal human intestinal flora(along with Klebsiella species and Escherichia coli),it is known to cause serious infections in humans,with a fatality rate of 20%-50%[1].Proteus mirabilis is intrinsically resistant to tetracycline,tigecycline,and colistin.The widespread and irregular use of antimicrobial agents,P.mirabilis antimicrobial resistance has mirabilis.They acquire antimicrobial resistance(AMR)by capturing plasmids,transposons,or other mobile elements harboring antimicrobial resistance genes.This mechanism allows the rapid selection and transmission of numerous AMR genes in clinical,veterinary,food production,transportation,and environmental settings.展开更多
Adenine nucleotide translocator(ANT)is a mitochondrial protein involved in the exchange of ADP and ATP across the mitochondrial inner membrane.It plays a crucial role in cellular energy metabolism by facilitating the ...Adenine nucleotide translocator(ANT)is a mitochondrial protein involved in the exchange of ADP and ATP across the mitochondrial inner membrane.It plays a crucial role in cellular energy metabolism by facilitating the transport of ATP synthesized within the mitochondria to the cytoplasm.The isoform ANT1 predominately expresses in cardiac and skeletal muscles.Mutations or dysregulation in ANT1 have been implicated in various mitochondrial disorders and neuromuscular diseases.We aimed to examine whether ANT1 deletion may affect mitochondrial redox state in our established ANT1-de-cient mice.Hearts and quadriceps resected from age-matched wild type(WT)and ANT1-de-cient mice were snap-frozen in liquid nitrogen.The Chance redox scanner was utilized to perform 3D optical redox imaging.Each sample underwent scanning across 3–5 sections.Global averaging analysis showed no signi-cant differences in the redox indices(NADH,flavin adenine dinucleotide containing-flavoproteins Fp,and the redox ratio Fp/(NADH+Fp)between WT and ANT1-de-cient groups.However,quadriceps had higher Fp than hearts in both groups(p¼0:0004 and 0.01,respectively).Furthermore,the quadriceps were also more oxidized(a higher redox ratio)than hearts in WT group(p¼0:004).NADH levels were similar in all cases.Our data suggest that under non-stressful physical condition,the ANT1-de-cient muscle cells were in the same mitochondrial state as WT ones and that the signi-cant difference in the mitochondrial redox state between quadriceps and hearts found in WT might be diminished in ANT1-de-cient ones.Redox imaging of muscles under physical stress can be conducted in future.展开更多
This advanced paper presents a new approach to improving image steganography using the Ant Colony Optimization(ACO)algorithm.Image steganography,a technique of embedding hidden information in digital photographs,shoul...This advanced paper presents a new approach to improving image steganography using the Ant Colony Optimization(ACO)algorithm.Image steganography,a technique of embedding hidden information in digital photographs,should ideally achieve the dual purposes of maximum data hiding and maintenance of the integrity of the cover media so that it is least suspect.The contemporary methods of steganography are at best a compromise between these two.In this paper,we present our approach,entitled Ant Colony Optimization(ACO)-Least Significant Bit(LSB),which attempts to optimize the capacity in steganographic embedding.The approach makes use of a grayscale cover image to hide the confidential data with an additional bit pair per byte,both for integrity verification and the file checksumof the secret data.This approach encodes confidential information into four pairs of bits and embeds it within uncompressed grayscale images.The ACO algorithm uses adaptive exploration to select some pixels,maximizing the capacity of data embedding whileminimizing the degradation of visual quality.Pheromone evaporation is introduced through iterations to avoid stagnation in solution refinement.The levels of pheromone are modified to reinforce successful pixel choices.Experimental results obtained through the ACO-LSB method reveal that it clearly improves image steganography capabilities by providing an increase of up to 30%in the embedding capacity compared with traditional approaches;the average Peak Signal to Noise Ratio(PSNR)is 40.5 dB with a Structural Index Similarity(SSIM)of 0.98.The approach also demonstrates very high resistance to detection,cutting down the rate by 20%.Implemented in MATLAB R2023a,the model was tested against one thousand publicly available grayscale images,thus providing robust evidence of its effectiveness.展开更多
This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic ...This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic Algorithm(GA).MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated solutions.The uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO.A first-time hybrid of these algorithms is employed to solve multi-objective problems.The hybrid algorithm overcomes the limitation of ALO of getting caught in the local optimum and the requirement of more computational effort to converge GA.To evaluate the hybridized algorithm’s performance,a set of constrained,unconstrained test problems and engineering design problems were employed and compared with five well-known computational algorithms-MOALO,Multi-objective Crystal Structure Algorithm(MOCryStAl),Multi-objective Particle Swarm Optimization(MOPSO),Multi-objective Multiverse Optimization Algorithm(MOMVO),Multi-objective Salp Swarm Algorithm(MSSA).The outcomes of five performance metrics are statistically analyzed and the most efficient Pareto fronts comparison has been obtained.The proposed hybrid surpasses MOALO based on the results of hypervolume(HV),Spread,and Spacing.So primary objective of developing this hybrid approach has been achieved successfully.The proposed approach demonstrates superior performance on the test functions,showcasing robust convergence and comprehensive coverage that surpasses other existing algorithms.展开更多
The present research investigated a segment of the micro-arthropod populations residing within nests of Messor arenarius ants in the Negev Desert of Israel. The total frequencies of micro-arthropods in the chaff of th...The present research investigated a segment of the micro-arthropod populations residing within nests of Messor arenarius ants in the Negev Desert of Israel. The total frequencies of micro-arthropods in the chaff of those ants’ nests were found to be higher than in the surrounding soil of the same nests. Acari (mites) were observed to be more abundant during the spring season, whereas their presence decreased during the summer months. Springtails (Collembola) were found to follow the Acari pattern, commonly found within the nests of those ants during spring but were absent during summer. Psocoptera order inhabiting soil habitats were infrequently encountered during spring, but their prevalence increased significantly during summer, particularly within the chaff of the ants’ nests, suggesting that chaff is their primary food source in the Negev Desert. Our research suggests that shifts in seasonality have important consequences on the distribution of soil invertebrate communities with implications on nutrient cycling.展开更多
As electro-hydrostatic actuator(EHA)technology advances towards lightweight and integration,the demand for enhanced internal flow pathways in hydraulic valve blocks intensifies.However,owing to the constraints imposed...As electro-hydrostatic actuator(EHA)technology advances towards lightweight and integration,the demand for enhanced internal flow pathways in hydraulic valve blocks intensifies.However,owing to the constraints imposed by traditional manufacturing processes,conventional hydraulic integrated valve blocks fail to satisfy the demands of a more compact channel layout and lower energy dissipation.Notably,the subjectivity in the arrangement of internal passages results in a time-consuming and labor-intensive process.This study employed additive manufacturing technology and the ant colony algorithm and B-spline curves for the meticulous design of internal passages within an aviation EHA valve block.The layout environment for the valve block passages was established,and path optimization was achieved using the ant colony algorithm,complemented by smoothing using B-spline curves.Three-dimensional modeling was performed using SolidWorks software,revealing a 10.03%reduction in volume for the optimized passages compared with the original passages.Computational fluid dynamics(CFD)simulations were performed using Fluent software,demonstrating that the algorithmically optimized passages effectively prevented the occurrence of vortices at right-angled locations,exhibited superior flow characteristics,and concurrently reduced pressure losses by 34.09%-36.36%.The small discrepancy between the experimental and simulation results validated the efficacy of the ant colony algorithm and B-spline curves in optimizing the passage design,offering a viable solution for channel design in additive manufacturing.展开更多
The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challengi...The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challenging.Feature selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant features.The ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection,because of its simplicity,efficiency,and similarity to reinforcement learning.Nevertheless,existing methods do not consider crucial correlation information,such as dynamic redundancy and label correlation.To tackle these concerns,the paper proposes a multi-label feature selection technique based on ant colony optimization algorithm(MFACO),focusing on dynamic redundancy and label correlation.Initially,the dynamic redundancy is assessed between the selected feature subset and potential features.Meanwhile,the ant colony optimization algorithm extracts label correlation from the label set,which is then combined into the heuristic factor as label weights.Experimental results demonstrate that our proposed strategies can effectively enhance the optimal search ability of ant colony,outperforming the other algorithms involved in the paper.展开更多
Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the node...Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the network.This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability.The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base station.The performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy usage.Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future.The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE).展开更多
In order to solve the problem of path planning of tower cranes,an improved ant colony algorithm was proposed.Firstly,the tower crane was simplified into a three-degree-of-freedom mechanical arm,and the D-H motion mode...In order to solve the problem of path planning of tower cranes,an improved ant colony algorithm was proposed.Firstly,the tower crane was simplified into a three-degree-of-freedom mechanical arm,and the D-H motion model was established to solve the forward and inverse kinematic equations.Secondly,the traditional ant colony algorithm was improved.The heuristic function was improved by introducing the distance between the optional nodes and the target point into the function.Then the transition probability was improved by introducing the security factor of surrounding points into the transition probability.In addition,the local path chunking strategy was used to optimize the local multi-inflection path and reduce the local redundant inflection points.Finally,according to the position of the hook,the kinematic inversion of the tower crane was carried out,and the variables of each joint were obtained.More specifically,compared with the traditional ant colony algorithm,the simulation results showed that improved ant colony algorithm converged faster,shortened the optimal path length,and optimized the path quality in the simple and complex environment.展开更多
Marine container terminal(MCT)plays a key role in the marine intelligent transportation system and international logistics system.However,the efficiency of resource scheduling significantly influences the operation pe...Marine container terminal(MCT)plays a key role in the marine intelligent transportation system and international logistics system.However,the efficiency of resource scheduling significantly influences the operation performance of MCT.To solve the practical resource scheduling problem(RSP)in MCT efficiently,this paper has contributions to both the problem model and the algorithm design.Firstly,in the problem model,different from most of the existing studies that only consider scheduling part of the resources in MCT,we propose a unified mathematical model for formulating an integrated RSP.The new integrated RSP model allocates and schedules multiple MCT resources simultaneously by taking the total cost minimization as the objective.Secondly,in the algorithm design,a pre-selection-based ant colony system(PACS)approach is proposed based on graphic structure solution representation and a pre-selection strategy.On the one hand,as the RSP can be formulated as the shortest path problem on the directed complete graph,the graphic structure is proposed to represent the solution encoding to consider multiple constraints and multiple factors of the RSP,which effectively avoids the generation of infeasible solutions.On the other hand,the pre-selection strategy aims to reduce the computational burden of PACS and to fast obtain a higher-quality solution.To evaluate the performance of the proposed novel PACS in solving the new integrated RSP model,a set of test cases with different sizes is conducted.Experimental results and comparisons show the effectiveness and efficiency of the PACS algorithm,which can significantly outperform other state-of-the-art algorithms.展开更多
Adaptability and dynamicity are special properties of social insects derived from the decentralized behavior of the insects. Authors have come up with designs for software solution that can regulate traffic congestion...Adaptability and dynamicity are special properties of social insects derived from the decentralized behavior of the insects. Authors have come up with designs for software solution that can regulate traffic congestion in a network transportation environment. The effectiveness of various researches on traffic management has been verified through appropriate metrics. Most of the traffic management systems are centered on using sensors, visual monitoring and neural networks to check for available parking space with the aim of informing drivers beforehand to prevent traffic congestion. There has been limited research on solving ongoing traffic congestion in congestion prone areas like car park with any of the common methods mentioned. This study focus however is on a motor park, as a highly congested area when it comes to traffic. The car park has two entrance gate and three exit gates which is divided into three Isle of parking lot where cars can park. An ant colony optimization algorithm (ACO) was developed as an effective management system for controlling navigation and vehicular traffic congestion problems when cars exit a motor park. The ACO based on the nature and movement of the natural ants, simulates the movement of cars out of the car park through their nearest choice exit. A car park simulation was also used for the mathematical computation of the pheromone. The system was implemented using SIMD because of its dual parallelization ability. The result showed about 95% increase on the number of vehicles that left the motor park in one second. A clear indication that pheromones are large determinants of the shortest route to take as cars followed the closest exit to them. Future researchers may consider monitoring a centralized tally system for cars coming into the park through a censored gate being.展开更多
With the rise of image data and increased complexity of tasks in edge detection, conventional artificial intelligence techniques have been severely impacted. To be able to solve even greater problems of the future, le...With the rise of image data and increased complexity of tasks in edge detection, conventional artificial intelligence techniques have been severely impacted. To be able to solve even greater problems of the future, learning algorithms must maintain high speed and accuracy through economical means. Traditional edge detection approaches cannot detect edges in images in a timely manner due to memory and computational time constraints. In this work, a novel parallelized ant colony optimization technique in a distributed framework provided by the Hadoop/Map-Reduce infrastructure is proposed to improve the edge detection capabilities. Moreover, a filtering technique is applied to reduce the noisy background of images to achieve significant improvement in the accuracy of edge detection. Close examinations of the implementation of the proposed algorithm are discussed and demonstrated through experiments. Results reveal high classification accuracy and significant improvements in speedup, scaleup and sizeup compared to the standard algorithms.展开更多
文摘The trade-off between users’ fairness and network throughput may be unacceptable in a multi-rate 802.11 WLAN environment. In this paper, we will design a new intuitive simplified mathematical model called simplified coefficient of variation (SCV) to closely reflect our topic. Through controlling the power of Access Points, SCV can optimize and improve the performance. Since our topic is a NP-hard problem, we use Ant Colony Algorithm to solve our model in a practical scenario. The simulation shows excellent results indicating that our model is efficient and superior to an existing method. Also we use software SAS to further reveal the relationships among the three indicators to illustrate the essence of our approach and an existing algorithm.
基金the National Key Research and Development Program of China[grant number 2021YFC2302002]for financial support.
文摘Proteus mirabilis is abundant in soil and water,and although it is part of the normal human intestinal flora(along with Klebsiella species and Escherichia coli),it is known to cause serious infections in humans,with a fatality rate of 20%-50%[1].Proteus mirabilis is intrinsically resistant to tetracycline,tigecycline,and colistin.The widespread and irregular use of antimicrobial agents,P.mirabilis antimicrobial resistance has mirabilis.They acquire antimicrobial resistance(AMR)by capturing plasmids,transposons,or other mobile elements harboring antimicrobial resistance genes.This mechanism allows the rapid selection and transmission of numerous AMR genes in clinical,veterinary,food production,transportation,and environmental settings.
基金supported in part by NIH Grant CA191207 and CA277037(L.Z.Li)AG078814 and CA259635(D.Wallace)and DOD Grant W81XWH2210561(D.Wallace).
文摘Adenine nucleotide translocator(ANT)is a mitochondrial protein involved in the exchange of ADP and ATP across the mitochondrial inner membrane.It plays a crucial role in cellular energy metabolism by facilitating the transport of ATP synthesized within the mitochondria to the cytoplasm.The isoform ANT1 predominately expresses in cardiac and skeletal muscles.Mutations or dysregulation in ANT1 have been implicated in various mitochondrial disorders and neuromuscular diseases.We aimed to examine whether ANT1 deletion may affect mitochondrial redox state in our established ANT1-de-cient mice.Hearts and quadriceps resected from age-matched wild type(WT)and ANT1-de-cient mice were snap-frozen in liquid nitrogen.The Chance redox scanner was utilized to perform 3D optical redox imaging.Each sample underwent scanning across 3–5 sections.Global averaging analysis showed no signi-cant differences in the redox indices(NADH,flavin adenine dinucleotide containing-flavoproteins Fp,and the redox ratio Fp/(NADH+Fp)between WT and ANT1-de-cient groups.However,quadriceps had higher Fp than hearts in both groups(p¼0:0004 and 0.01,respectively).Furthermore,the quadriceps were also more oxidized(a higher redox ratio)than hearts in WT group(p¼0:004).NADH levels were similar in all cases.Our data suggest that under non-stressful physical condition,the ANT1-de-cient muscle cells were in the same mitochondrial state as WT ones and that the signi-cant difference in the mitochondrial redox state between quadriceps and hearts found in WT might be diminished in ANT1-de-cient ones.Redox imaging of muscles under physical stress can be conducted in future.
文摘This advanced paper presents a new approach to improving image steganography using the Ant Colony Optimization(ACO)algorithm.Image steganography,a technique of embedding hidden information in digital photographs,should ideally achieve the dual purposes of maximum data hiding and maintenance of the integrity of the cover media so that it is least suspect.The contemporary methods of steganography are at best a compromise between these two.In this paper,we present our approach,entitled Ant Colony Optimization(ACO)-Least Significant Bit(LSB),which attempts to optimize the capacity in steganographic embedding.The approach makes use of a grayscale cover image to hide the confidential data with an additional bit pair per byte,both for integrity verification and the file checksumof the secret data.This approach encodes confidential information into four pairs of bits and embeds it within uncompressed grayscale images.The ACO algorithm uses adaptive exploration to select some pixels,maximizing the capacity of data embedding whileminimizing the degradation of visual quality.Pheromone evaporation is introduced through iterations to avoid stagnation in solution refinement.The levels of pheromone are modified to reinforce successful pixel choices.Experimental results obtained through the ACO-LSB method reveal that it clearly improves image steganography capabilities by providing an increase of up to 30%in the embedding capacity compared with traditional approaches;the average Peak Signal to Noise Ratio(PSNR)is 40.5 dB with a Structural Index Similarity(SSIM)of 0.98.The approach also demonstrates very high resistance to detection,cutting down the rate by 20%.Implemented in MATLAB R2023a,the model was tested against one thousand publicly available grayscale images,thus providing robust evidence of its effectiveness.
基金supported by the National Research Foundation of Korea(NRF)Grant funded by the Korea government(MSIT)(No.RS-2023-00218176)the Soonchunhyang University Research Fund.
文摘This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic Algorithm(GA).MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated solutions.The uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO.A first-time hybrid of these algorithms is employed to solve multi-objective problems.The hybrid algorithm overcomes the limitation of ALO of getting caught in the local optimum and the requirement of more computational effort to converge GA.To evaluate the hybridized algorithm’s performance,a set of constrained,unconstrained test problems and engineering design problems were employed and compared with five well-known computational algorithms-MOALO,Multi-objective Crystal Structure Algorithm(MOCryStAl),Multi-objective Particle Swarm Optimization(MOPSO),Multi-objective Multiverse Optimization Algorithm(MOMVO),Multi-objective Salp Swarm Algorithm(MSSA).The outcomes of five performance metrics are statistically analyzed and the most efficient Pareto fronts comparison has been obtained.The proposed hybrid surpasses MOALO based on the results of hypervolume(HV),Spread,and Spacing.So primary objective of developing this hybrid approach has been achieved successfully.The proposed approach demonstrates superior performance on the test functions,showcasing robust convergence and comprehensive coverage that surpasses other existing algorithms.
文摘The present research investigated a segment of the micro-arthropod populations residing within nests of Messor arenarius ants in the Negev Desert of Israel. The total frequencies of micro-arthropods in the chaff of those ants’ nests were found to be higher than in the surrounding soil of the same nests. Acari (mites) were observed to be more abundant during the spring season, whereas their presence decreased during the summer months. Springtails (Collembola) were found to follow the Acari pattern, commonly found within the nests of those ants during spring but were absent during summer. Psocoptera order inhabiting soil habitats were infrequently encountered during spring, but their prevalence increased significantly during summer, particularly within the chaff of the ants’ nests, suggesting that chaff is their primary food source in the Negev Desert. Our research suggests that shifts in seasonality have important consequences on the distribution of soil invertebrate communities with implications on nutrient cycling.
基金Supported by National Natural Science Foundation of China(Grant No.51890881)。
文摘As electro-hydrostatic actuator(EHA)technology advances towards lightweight and integration,the demand for enhanced internal flow pathways in hydraulic valve blocks intensifies.However,owing to the constraints imposed by traditional manufacturing processes,conventional hydraulic integrated valve blocks fail to satisfy the demands of a more compact channel layout and lower energy dissipation.Notably,the subjectivity in the arrangement of internal passages results in a time-consuming and labor-intensive process.This study employed additive manufacturing technology and the ant colony algorithm and B-spline curves for the meticulous design of internal passages within an aviation EHA valve block.The layout environment for the valve block passages was established,and path optimization was achieved using the ant colony algorithm,complemented by smoothing using B-spline curves.Three-dimensional modeling was performed using SolidWorks software,revealing a 10.03%reduction in volume for the optimized passages compared with the original passages.Computational fluid dynamics(CFD)simulations were performed using Fluent software,demonstrating that the algorithmically optimized passages effectively prevented the occurrence of vortices at right-angled locations,exhibited superior flow characteristics,and concurrently reduced pressure losses by 34.09%-36.36%.The small discrepancy between the experimental and simulation results validated the efficacy of the ant colony algorithm and B-spline curves in optimizing the passage design,offering a viable solution for channel design in additive manufacturing.
基金supported by National Natural Science Foundation of China(Grant Nos.62376089,62302153,62302154,62202147)the key Research and Development Program of Hubei Province,China(Grant No.2023BEB024).
文摘The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challenging.Feature selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant features.The ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection,because of its simplicity,efficiency,and similarity to reinforcement learning.Nevertheless,existing methods do not consider crucial correlation information,such as dynamic redundancy and label correlation.To tackle these concerns,the paper proposes a multi-label feature selection technique based on ant colony optimization algorithm(MFACO),focusing on dynamic redundancy and label correlation.Initially,the dynamic redundancy is assessed between the selected feature subset and potential features.Meanwhile,the ant colony optimization algorithm extracts label correlation from the label set,which is then combined into the heuristic factor as label weights.Experimental results demonstrate that our proposed strategies can effectively enhance the optimal search ability of ant colony,outperforming the other algorithms involved in the paper.
文摘Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the network.This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability.The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base station.The performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy usage.Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future.The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE).
基金supported by Shaanxi Provincial Key Research and Development Program of China(Nos.2024GX-YBXM-305,2024GX-YBXM-178)Shaanxi Province Qinchuangyuan“Scientists+Engineers”Team Construction(No.2022KXJ032)。
文摘In order to solve the problem of path planning of tower cranes,an improved ant colony algorithm was proposed.Firstly,the tower crane was simplified into a three-degree-of-freedom mechanical arm,and the D-H motion model was established to solve the forward and inverse kinematic equations.Secondly,the traditional ant colony algorithm was improved.The heuristic function was improved by introducing the distance between the optional nodes and the target point into the function.Then the transition probability was improved by introducing the security factor of surrounding points into the transition probability.In addition,the local path chunking strategy was used to optimize the local multi-inflection path and reduce the local redundant inflection points.Finally,according to the position of the hook,the kinematic inversion of the tower crane was carried out,and the variables of each joint were obtained.More specifically,compared with the traditional ant colony algorithm,the simulation results showed that improved ant colony algorithm converged faster,shortened the optimal path length,and optimized the path quality in the simple and complex environment.
基金This research was supported in part by the National Key Research and Development Program of China under Grant 2022YFB3305303in part by the National Natural Science Foundations of China(NSFC)under Grant 62106055+1 种基金in part by the Guangdong Natural Science Foundation under Grant 2022A1515011825in part by the Guangzhou Science and Technology Planning Project under Grants 2023A04J0388 and 2023A03J0662.
文摘Marine container terminal(MCT)plays a key role in the marine intelligent transportation system and international logistics system.However,the efficiency of resource scheduling significantly influences the operation performance of MCT.To solve the practical resource scheduling problem(RSP)in MCT efficiently,this paper has contributions to both the problem model and the algorithm design.Firstly,in the problem model,different from most of the existing studies that only consider scheduling part of the resources in MCT,we propose a unified mathematical model for formulating an integrated RSP.The new integrated RSP model allocates and schedules multiple MCT resources simultaneously by taking the total cost minimization as the objective.Secondly,in the algorithm design,a pre-selection-based ant colony system(PACS)approach is proposed based on graphic structure solution representation and a pre-selection strategy.On the one hand,as the RSP can be formulated as the shortest path problem on the directed complete graph,the graphic structure is proposed to represent the solution encoding to consider multiple constraints and multiple factors of the RSP,which effectively avoids the generation of infeasible solutions.On the other hand,the pre-selection strategy aims to reduce the computational burden of PACS and to fast obtain a higher-quality solution.To evaluate the performance of the proposed novel PACS in solving the new integrated RSP model,a set of test cases with different sizes is conducted.Experimental results and comparisons show the effectiveness and efficiency of the PACS algorithm,which can significantly outperform other state-of-the-art algorithms.
文摘Adaptability and dynamicity are special properties of social insects derived from the decentralized behavior of the insects. Authors have come up with designs for software solution that can regulate traffic congestion in a network transportation environment. The effectiveness of various researches on traffic management has been verified through appropriate metrics. Most of the traffic management systems are centered on using sensors, visual monitoring and neural networks to check for available parking space with the aim of informing drivers beforehand to prevent traffic congestion. There has been limited research on solving ongoing traffic congestion in congestion prone areas like car park with any of the common methods mentioned. This study focus however is on a motor park, as a highly congested area when it comes to traffic. The car park has two entrance gate and three exit gates which is divided into three Isle of parking lot where cars can park. An ant colony optimization algorithm (ACO) was developed as an effective management system for controlling navigation and vehicular traffic congestion problems when cars exit a motor park. The ACO based on the nature and movement of the natural ants, simulates the movement of cars out of the car park through their nearest choice exit. A car park simulation was also used for the mathematical computation of the pheromone. The system was implemented using SIMD because of its dual parallelization ability. The result showed about 95% increase on the number of vehicles that left the motor park in one second. A clear indication that pheromones are large determinants of the shortest route to take as cars followed the closest exit to them. Future researchers may consider monitoring a centralized tally system for cars coming into the park through a censored gate being.
文摘With the rise of image data and increased complexity of tasks in edge detection, conventional artificial intelligence techniques have been severely impacted. To be able to solve even greater problems of the future, learning algorithms must maintain high speed and accuracy through economical means. Traditional edge detection approaches cannot detect edges in images in a timely manner due to memory and computational time constraints. In this work, a novel parallelized ant colony optimization technique in a distributed framework provided by the Hadoop/Map-Reduce infrastructure is proposed to improve the edge detection capabilities. Moreover, a filtering technique is applied to reduce the noisy background of images to achieve significant improvement in the accuracy of edge detection. Close examinations of the implementation of the proposed algorithm are discussed and demonstrated through experiments. Results reveal high classification accuracy and significant improvements in speedup, scaleup and sizeup compared to the standard algorithms.
