Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the chall...Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios.In this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)algorithm.The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm.Comprehensive testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been done.The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm.Compared with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local optima.As a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental.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
Distribution generation(DG)technology based on a variety of renewable energy technologies has developed rapidly.A large number of multi-type DG are connected to the distribution network(DN),resulting in a decline in t...Distribution generation(DG)technology based on a variety of renewable energy technologies has developed rapidly.A large number of multi-type DG are connected to the distribution network(DN),resulting in a decline in the stability of DN operation.It is urgent to find a method that can effectively connect multi-energy DG to DN.photovoltaic(PV),wind power generation(WPG),fuel cell(FC),and micro gas turbine(MGT)are considered in this paper.A multi-objective optimization model was established based on the life cycle cost(LCC)of DG,voltage quality,voltage fluctuation,system network loss,power deviation of the tie-line,DG pollution emission index,and meteorological index weight of DN.Multi-objective artificial bee colony algorithm(MOABC)was used to determine the optimal location and capacity of the four kinds of DG access DN,and compared with the other three heuristic algorithms.Simulation tests based on IEEE 33 test node and IEEE 69 test node show that in IEEE 33 test node,the total voltage deviation,voltage fluctuation,and system network loss of DN decreased by 49.67%,7.47%and 48.12%,respectively,compared with that without DG configuration.In the IEEE 69 test node,the total voltage deviation,voltage fluctuation and system network loss of DN in the MOABC configuration scheme decreased by 54.98%,35.93%and 75.17%,respectively,compared with that without DG configuration,indicating that MOABC can reasonably plan the capacity and location of DG.Achieve the maximum trade-off between DG economy and DN operation stability.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
This article aims to understand the training process of history undergraduates,to see if there are decolonial curricular practices to combat racism at the Centro Universitário e Faculdade Projeção(UniPr...This article aims to understand the training process of history undergraduates,to see if there are decolonial curricular practices to combat racism at the Centro Universitário e Faculdade Projeção(UniProjeção)in the Federal District,to understand how coloniality has corroborated the exclusion of different epistemologies and the erasure of different cultures,and how this exclusionary process of coloniality interferes in the training of history teachers.In order to combat this practice,we are looking for alternatives that can break these suppressions carried out by Europeans.In this way,we turn to decolonial ideas that aim to break with the logic of coloniality.We can conclude that these practices are poorly developed in the institution,so we proposed active problem-based methodology and music as a didactic resource.As playful educational tools that strengthen the teaching-learning process,they are active agents in the decolonial work of combating racism,and it is essential to train responsible and ethical teachers in the fight against racism and any form of oppression.展开更多
Circulating tumor cells (CTC) are rarely detected in the blood of cancer patients, even though they are a direct harbinger of eventual patient demise. We developed an innovative CTC culture technology to allow more se...Circulating tumor cells (CTC) are rarely detected in the blood of cancer patients, even though they are a direct harbinger of eventual patient demise. We developed an innovative CTC culture technology to allow more sensitive isolation, expansion, and characterization of viable colonies from patient blood. In this assay, the entire leukocyte fraction from 10 ml of anticoagulated patient blood is placed into culture medium without any pre-selection. After 16 days in culture, CTC derived colonies are counted. As a proof-of-principle, blood samples from 58 Stage IIa-IV melanoma patients were tested. Ninety percent of these samples grew colonies. The colony numbers ranged from 0 - 308 (mean 63 ± 9.5 SEM). Ten normal volunteers had virtually no growth (mean 0.5 ± 1.4 colonies). Colonies were harvested using a micropipette for characterization. Tumor-cell containing spheroids were embedded in paraffin, sectioned, and stained with melanoma-specific mAb for histologic characterization. MITF proved to be the most consistent immunostain that identified melanoma cells in these colonies. A host-cell component in colonies was also identified using CD68 and CD43 mAb staining. Following enzymatic dissociation of colonies, a variety of immunostains were tested. Papanicolau staining proved most useful for identifying the abnormal nuclei of tumor cells. Flow cytometry could readily distinguish host and tumor cell populations based on DNA content and forward/side scatter in dissociated colonies. The stem cell marker ALDH1A1 associated with the aneuploid population, but CD45 was expressed on both diploid and aneuploid cells. The ability to repeatedly isolate CTC derived colonies from cancer patient blood samples opens the door to a novel type of long-term clinical monitoring. This novel CTC culture technology may prove useful to perform molecular characterization, assessment of treatment response, and testing of drug sensitivity and resistance in patients during treatment.展开更多
To solve the complex weight matrix derivative problem when using the weighted least squares method to estimate the parameters of the mixed additive and multiplicative random error model(MAM error model),we use an impr...To solve the complex weight matrix derivative problem when using the weighted least squares method to estimate the parameters of the mixed additive and multiplicative random error model(MAM error model),we use an improved artificial bee colony algorithm without derivative and the bootstrap method to estimate the parameters and evaluate the accuracy of MAM error model.The improved artificial bee colony algorithm can update individuals in multiple dimensions and improve the cooperation ability between individuals by constructing a new search equation based on the idea of quasi-affine transformation.The experimental results show that based on the weighted least squares criterion,the algorithm can get the results consistent with the weighted least squares method without multiple formula derivation.The parameter estimation and accuracy evaluation method based on the bootstrap method can get better parameter estimation and more reasonable accuracy information than existing methods,which provides a new idea for the theory of parameter estimation and accuracy evaluation of the MAM error model.展开更多
Support vehicles are part of the main body of airport ground operations,and their scheduling efficiency directly impacts flight delays.A mathematical model is constructed and the responsiveness of support vehicles for...Support vehicles are part of the main body of airport ground operations,and their scheduling efficiency directly impacts flight delays.A mathematical model is constructed and the responsiveness of support vehicles for current operational demands is proposed to study optimization algorithms for vehicle scheduling.The model is based on the constraint relationship of the initial operation time,time window,and gate position distribution,which gives an improvement to the ant colony algorithm(ACO).The impacts of the improved ACO as used for support vehicle optimization are compared and analyzed.The results show that the scheduling scheme of refueling trucks based on the improved ACO can reduce flight delays caused by refueling operations by 56.87%,indicating the improved ACO can improve support vehicle scheduling.Besides,the improved ACO can jump out of local optima,which can balance the working time of refueling trucks.This research optimizes the scheduling scheme of support vehicles under the existing conditions of airports,which has practical significance to fully utilize ground service resources,improve the efficiency of airport ground operations,and effectively reduce flight delays caused by ground service support.展开更多
Securing digital image data is a key concern in today’s information-driven society.Effective encryption techniques are required to protect sensitive image data,with the Substitution-box(S-box)often playing a pivotal ...Securing digital image data is a key concern in today’s information-driven society.Effective encryption techniques are required to protect sensitive image data,with the Substitution-box(S-box)often playing a pivotal role in many symmetric encryption systems.This study introduces an innovative approach to creating S-boxes for encryption algorithms.The proposed S-boxes are tested for validity and non-linearity by incorporating them into an image encryption scheme.The nonlinearity measure of the proposed S-boxes is 112.These qualities significantly enhance its resistance to common cryptographic attacks,ensuring high image data security.Furthermore,to assess the robustness of the S-boxes,an encryption system has also been proposed and the proposed S-boxes have been integrated into the designed encryption system.To validate the effectiveness of the proposed encryption system,a comprehensive security analysis including brute force attack and histogram analysis has been performed.In addition,to determine the level of security during the transmission and storage of digital content,the encryption system’s Number of Pixel Change Rate(NPCR),and Unified Averaged Changed Intensity(UACI)are calculated.The results indicate a 99.71%NPCR and 33.51%UACI.These results demonstrate that the proposed S-boxes offer a significant level of security for digital content throughout its transmission and storage.展开更多
In this research,an Artificial Bee Colony(ABC)algorithm based Selective Harmonics Elimination(SHE)technique is used as a pulse generator in a reduced switch fifteen level inverter that receives input from a PV system....In this research,an Artificial Bee Colony(ABC)algorithm based Selective Harmonics Elimination(SHE)technique is used as a pulse generator in a reduced switch fifteen level inverter that receives input from a PV system.Pulse width modulation based on Selective Harmonics Elimination is mostly used to suppress lower-order harmonics.A high gain DC-DC-SEPIC converter keeps the photovoltaic(PV)panel’s output voltage constant.The Grey Wolf Optimization(GWO)filter removes far more Photovoltaic panel energy from the sunlight frame.To eliminate voltage harmonics,this unique inverter architecture employs a multi-carrier duty cycle,a high-frequency modulation approach.The proposed ABC harmonics elimination approach is compared to SHE strategies based on Particle Swarm Optimization(PSO)and Flower Pollination Algorithm(FPA).The suggested system’s performance is simulated and measured using the MATLAB simulation tool.The proposed ABC approach has a THD level of 4.86%,which is better than the PSO and FPA methods.展开更多
This article presents an optimized approach of mathematical techniques in themedical domain by manoeuvring the phenomenon of ant colony optimization algorithm(also known as ACO).A complete graph of blood banks and a p...This article presents an optimized approach of mathematical techniques in themedical domain by manoeuvring the phenomenon of ant colony optimization algorithm(also known as ACO).A complete graph of blood banks and a path that covers all the blood banks without repeating any link is required by applying the Travelling Salesman Problem(often TSP).The wide use promises to accelerate and offers the opportunity to cultivate health care,particularly in remote or unmerited environments by shrinking lab testing reversal times,empowering just-in-time lifesaving medical supply.展开更多
Security testing is a critical concern for organizations worldwide due to the potential financial setbacks and damage to reputation caused by insecure software systems.One of the challenges in software security testin...Security testing is a critical concern for organizations worldwide due to the potential financial setbacks and damage to reputation caused by insecure software systems.One of the challenges in software security testing is test case prioritization,which aims to reduce redundancy in fault occurrences when executing test suites.By effectively applying test case prioritization,both the time and cost required for developing secure software can be reduced.This paper proposes a test case prioritization technique based on the Ant Colony Optimization(ACO)algorithm,a metaheuristic approach.The performance of the ACO-based technique is evaluated using the Average Percentage of Fault Detection(APFD)metric,comparing it with traditional techniques.It has been applied to a Mobile Payment Wallet application to validate the proposed approach.The results demonstrate that the proposed technique outperforms the traditional techniques in terms of the APFD metric.The ACO-based technique achieves an APFD of approximately 76%,two percent higher than the second-best optimal ordering technique.These findings suggest that metaheuristic-based prioritization techniques can effectively identify the best test cases,saving time and improving software security overall.展开更多
Background:Recently,researchers have been attracted in identifying the crucial genes related to cancer,which plays important role in cancer diagnosis and treatment.However,in performing the cancer molecular subtype cl...Background:Recently,researchers have been attracted in identifying the crucial genes related to cancer,which plays important role in cancer diagnosis and treatment.However,in performing the cancer molecular subtype classification task from cancer gene expression data,it is challenging to obtain those significant genes due to the high dimensionality and high noise of data.Moreover,the existing methods always suffer from some issues such as premature convergence.Methods:To address those problems,we propose a new ant colony optimization(ACO)algorithm called DACO to classify the cancer gene expression datasets,identifying the essential genes of different diseases.In DACO,first,we propose the initial pheromone concentration based on the weight ranking vector to accelerate the convergence speed;then,a dynamic pheromone volatility factor is designed to prevent the algorithm from getting stuck in the local optimal solution;finally,the pheromone update rule in the Ant Colony System is employed to update the pheromone globally and locally.To demonstrate the performance of the proposed algorithm in classification,different existing approaches are compared with the proposed algorithm on eight high-dimensional cancer gene expression datasets.Results:The experiment results show that the proposed algorithm performs better than other effective methods in terms of classification accuracy and the number of feature sets.It can be used to address the classification problem effectively.Moreover,a renal cell carcinoma dataset is employed to reveal the biological significance of the proposed algorithm from a number of biological analyses.Conclusion:The results demonstrate that CAPS may play a crucial role in the occurrence and development of renal clear cell carcinoma.展开更多
With the development of satellite communication,in order to solve the problems of shortage of on-board resources and refinement of delay requirements to improve the communication performance of satellite optical netwo...With the development of satellite communication,in order to solve the problems of shortage of on-board resources and refinement of delay requirements to improve the communication performance of satellite optical networks,this paper proposes a bee colony optimization algorithm for routing and wavelength assignment based on directional guidance(DBCO-RWA)in satellite optical networks.In D-BCORWA,directional guidance based on relative position and link load is defined,and then the link cost function in the path search stage is established based on the directional guidance factor.Finally,feasible solutions are expanded in the global optimization stage.The wavelength utilization,communication success probability,blocking rate,communication hops and convergence characteristic are simulated.The results show that the performance of the proposed algorithm is improved compared with existing algorithms.展开更多
In the Internet of Things(IoT),the users have complex needs,and the Web Service Composition(WSC)was introduced to address these needs.The WSC’s main objective is to search for the optimal combination of web services ...In the Internet of Things(IoT),the users have complex needs,and the Web Service Composition(WSC)was introduced to address these needs.The WSC’s main objective is to search for the optimal combination of web services in response to the user needs and the level of Quality of Services(QoS)constraints.The challenge of this problem is the huge number of web services that achieve similar functionality with different levels of QoS constraints.In this paper,we introduce an extension of our previous works on the Artificial Bee Colony(ABC)and Bat Algorithm(BA).A new hybrid algorithm was proposed between the ABC and BA to achieve a better tradeoff between local exploitation and global search.The bat agent is used to improve the solution of exhausted bees after a threshold(limits),and also an Elitist Strategy(ES)is added to BA to increase the convergence rate.The performance and convergence behavior of the proposed hybrid algorithm was tested using extensive comparative experiments with current state-ofthe-art nature-inspired algorithms on 12 benchmark datasets using three evaluation criteria(average fitness values,best fitness values,and execution time)that were measured for 30 different runs.These datasets are created from real-world datasets and artificially to form different scale sizes of WSC datasets.The results show that the proposed algorithm enhances the search performance and convergence rate on finding the near-optimal web services combination compared to competitors.TheWilcoxon signed-rank significant test is usedwhere the proposed algorithm results significantly differ fromother algorithms on 100%of datasets.展开更多
In recent years,cloud computing has provided a Software As A Service(SaaS)platform where the software can be reused and applied to fulfill compli-cated user demands according to specific Quality of Services(QoS)constrai...In recent years,cloud computing has provided a Software As A Service(SaaS)platform where the software can be reused and applied to fulfill compli-cated user demands according to specific Quality of Services(QoS)constraints.The user requirements are formulated as a workflow consisting of a set of tasks.However,many services may satisfy the functionality of each task;thus,searching for the composition of the optimal service while maximizing the QoS is formulated as an NP-hard problem.This work will introduce a hybrid Artificial Bee Colony(ABC)with a Cuckoo Search(CS)algorithm to untangle service composition problem.The ABC is a well-known metaheuristic algorithm that can be applied when dealing with different NP-hard problems with an outstanding record of performance.However,the ABC suffers from a slow convergence problem.Therefore,the CS is used to overcome the ABC’s limitations by allowing the abandoned bees to enhance their search and override the local optimum.The proposed hybrid algorithm has been tested on 19 datasets and then compared with two standard algorithms(ABC and CS)and three state-of-the-art swarm-based composition algorithms.In addition,extensive parameter study experiments were conducted to set up the proposed algorithm’s parameters.The results indicate that the proposed algorithm outperforms the standard algorithms in the three comparison criteria(bestfitness value,averagefitness value,and average execution time)overall datasets in 30 different runs.Furthermore,the proposed algorithm also exhibits better performance than the state–of–the–art algorithms in the three comparison criteria over 30 different runs.展开更多
A fusion algorithm is proposed to enhance the search speed of an ant colony system(ACS)for the global path planning and overcome the challenges of the local path planning in an unmanned aerial vehicle(UAV).The ACS sea...A fusion algorithm is proposed to enhance the search speed of an ant colony system(ACS)for the global path planning and overcome the challenges of the local path planning in an unmanned aerial vehicle(UAV).The ACS search efficiency is enhanced by adopting a 16-direction 24-neighborhood search way,a safety grid search way,and an elite hybrid strategy to accelerate global convergence.Quadratic planning is performed using the moving average(MA)method.The fusion algorithm incorporates a dynamic window approach(DWA)to deal with the local path planning,sets a retracement mechanism,and adjusts the evaluation function accordingly.Experimental results in two environments demonstrate that the improved ant colony system(IACS)achieves superior planning efficiency.Additionally,the optimized dynamic window approach(ODWA)demonstrates its ability to handle multiple dynamic situations.Overall,the fusion optimization algorithm can accomplish the mixed path planning effectively.展开更多
With the advancement of combat equipment technology and combat concepts,new requirements have been put forward for air defense operations during a group target attack.To achieve high-efficiency and lowloss defensive o...With the advancement of combat equipment technology and combat concepts,new requirements have been put forward for air defense operations during a group target attack.To achieve high-efficiency and lowloss defensive operations,a reasonable air defense weapon assignment strategy is a key step.In this paper,a multi-objective and multi-constraints weapon target assignment(WTA)model is established that aims to minimize the defensive resource loss,minimize total weapon consumption,and minimize the target residual effectiveness.An optimization framework of air defense weapon mission scheduling based on the multiobjective artificial bee colony(MOABC)algorithm is proposed.The solution for point-to-point saturated attack targets at different operational scales is achieved by encoding the nectar with real numbers.Simulations are performed for an imagined air defense scenario,where air defense weapons are saturated.The non-dominated solution sets are obtained by the MOABC algorithm to meet the operational demand.In the case where there are more weapons than targets,more diverse assignment schemes can be selected.According to the inverse generation distance(IGD)index,the convergence and diversity for the solutions of the non-dominated sorting genetic algorithm III(NSGA-III)algorithm and the MOABC algorithm are compared and analyzed.The results prove that the MOABC algorithm has better convergence and the solutions are more evenly distributed among the solution space.展开更多
A relationship between lung transplant success and many features of recipients’/donors has long been studied.However,modeling a robust model of a potential impact on organ transplant success has proved challenging.In...A relationship between lung transplant success and many features of recipients’/donors has long been studied.However,modeling a robust model of a potential impact on organ transplant success has proved challenging.In this study,a hybrid feature selection model was developed based on ant colony opti-mization(ACO)and k-nearest neighbor(kNN)classifier to investigate the rela-tionship between the most defining features of recipients/donors and lung transplant success using data from the United Network of Organ Sharing(UNOS).The proposed ACO-kNN approach explores the features space to identify the representative attributes and classify patients’functional status(i.e.,quality of life)after lung transplantation.The efficacy of the proposed model was verified using 3,684 records and 118 input features from the UNOS.The developed approach examined the reliability and validity of the lung allocation process.The results are promising regarding accuracy prediction to be 91.3%and low computational time,along with better decision capabilities,emphasizing the potential for automatic classification of the lung and other organs allocation pro-cesses.In addition,the proposed model recommends a new perspective on how medical experts and clinicians respond to uncertain and challenging lung alloca-tion strategies.Having such ACO-kNN model,a medical professional can sum-marize information through the proposed method and make decisions for the upcoming transplants to allocate the donor organ.展开更多
My essay on Heart of Darkness explores Conrad’s dramatization of the voice and heart dynamic in the novella as a penetration of the deep-lying rationale of colonial operation and more generally the human condition at...My essay on Heart of Darkness explores Conrad’s dramatization of the voice and heart dynamic in the novella as a penetration of the deep-lying rationale of colonial operation and more generally the human condition at the turn of the 20th century.Three different forms of assumed centrality in the novella-capital,Eurocentrism,and the African interior as detrimental void symbolized by the heart of darkness form concentric circles that interconnect and work in concert as the core colonial ideology and produce a sense of incongruity and absurdity.The voice of this colonial ideological apparatus disguised as the universal subject of humanity and rationality finds its best expression in the eloquence of Kurtz,the specimen of culture seduced by its very opposite-the wilderness-right in the heart of darkness.“The horror”,as his final verdict of life as a European with real intent of living on his own terms at the turn of the 20th century,sums up the predicament of a civilization with too much culture and self-absorption for its own good.展开更多
基金jointly supported by the Jiangsu Postgraduate Research and Practice Innovation Project under Grant KYCX22_1030,SJCX22_0283 and SJCX23_0293the NUPTSF under Grant NY220201.
文摘Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios.In this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)algorithm.The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm.Comprehensive testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been done.The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm.Compared with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local optima.As a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental.
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).
文摘Distribution generation(DG)technology based on a variety of renewable energy technologies has developed rapidly.A large number of multi-type DG are connected to the distribution network(DN),resulting in a decline in the stability of DN operation.It is urgent to find a method that can effectively connect multi-energy DG to DN.photovoltaic(PV),wind power generation(WPG),fuel cell(FC),and micro gas turbine(MGT)are considered in this paper.A multi-objective optimization model was established based on the life cycle cost(LCC)of DG,voltage quality,voltage fluctuation,system network loss,power deviation of the tie-line,DG pollution emission index,and meteorological index weight of DN.Multi-objective artificial bee colony algorithm(MOABC)was used to determine the optimal location and capacity of the four kinds of DG access DN,and compared with the other three heuristic algorithms.Simulation tests based on IEEE 33 test node and IEEE 69 test node show that in IEEE 33 test node,the total voltage deviation,voltage fluctuation,and system network loss of DN decreased by 49.67%,7.47%and 48.12%,respectively,compared with that without DG configuration.In the IEEE 69 test node,the total voltage deviation,voltage fluctuation and system network loss of DN in the MOABC configuration scheme decreased by 54.98%,35.93%and 75.17%,respectively,compared with that without DG configuration,indicating that MOABC can reasonably plan the capacity and location of DG.Achieve the maximum trade-off between DG economy and DN operation stability.
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This article aims to understand the training process of history undergraduates,to see if there are decolonial curricular practices to combat racism at the Centro Universitário e Faculdade Projeção(UniProjeção)in the Federal District,to understand how coloniality has corroborated the exclusion of different epistemologies and the erasure of different cultures,and how this exclusionary process of coloniality interferes in the training of history teachers.In order to combat this practice,we are looking for alternatives that can break these suppressions carried out by Europeans.In this way,we turn to decolonial ideas that aim to break with the logic of coloniality.We can conclude that these practices are poorly developed in the institution,so we proposed active problem-based methodology and music as a didactic resource.As playful educational tools that strengthen the teaching-learning process,they are active agents in the decolonial work of combating racism,and it is essential to train responsible and ethical teachers in the fight against racism and any form of oppression.
文摘Circulating tumor cells (CTC) are rarely detected in the blood of cancer patients, even though they are a direct harbinger of eventual patient demise. We developed an innovative CTC culture technology to allow more sensitive isolation, expansion, and characterization of viable colonies from patient blood. In this assay, the entire leukocyte fraction from 10 ml of anticoagulated patient blood is placed into culture medium without any pre-selection. After 16 days in culture, CTC derived colonies are counted. As a proof-of-principle, blood samples from 58 Stage IIa-IV melanoma patients were tested. Ninety percent of these samples grew colonies. The colony numbers ranged from 0 - 308 (mean 63 ± 9.5 SEM). Ten normal volunteers had virtually no growth (mean 0.5 ± 1.4 colonies). Colonies were harvested using a micropipette for characterization. Tumor-cell containing spheroids were embedded in paraffin, sectioned, and stained with melanoma-specific mAb for histologic characterization. MITF proved to be the most consistent immunostain that identified melanoma cells in these colonies. A host-cell component in colonies was also identified using CD68 and CD43 mAb staining. Following enzymatic dissociation of colonies, a variety of immunostains were tested. Papanicolau staining proved most useful for identifying the abnormal nuclei of tumor cells. Flow cytometry could readily distinguish host and tumor cell populations based on DNA content and forward/side scatter in dissociated colonies. The stem cell marker ALDH1A1 associated with the aneuploid population, but CD45 was expressed on both diploid and aneuploid cells. The ability to repeatedly isolate CTC derived colonies from cancer patient blood samples opens the door to a novel type of long-term clinical monitoring. This novel CTC culture technology may prove useful to perform molecular characterization, assessment of treatment response, and testing of drug sensitivity and resistance in patients during treatment.
基金supported by the National Natural Science Foundation of China(No.42174011 and No.41874001).
文摘To solve the complex weight matrix derivative problem when using the weighted least squares method to estimate the parameters of the mixed additive and multiplicative random error model(MAM error model),we use an improved artificial bee colony algorithm without derivative and the bootstrap method to estimate the parameters and evaluate the accuracy of MAM error model.The improved artificial bee colony algorithm can update individuals in multiple dimensions and improve the cooperation ability between individuals by constructing a new search equation based on the idea of quasi-affine transformation.The experimental results show that based on the weighted least squares criterion,the algorithm can get the results consistent with the weighted least squares method without multiple formula derivation.The parameter estimation and accuracy evaluation method based on the bootstrap method can get better parameter estimation and more reasonable accuracy information than existing methods,which provides a new idea for the theory of parameter estimation and accuracy evaluation of the MAM error model.
基金the Science and Technology Cooperation Research and Development Project of Sichuan Provincial Academy and University(Grant No.2019YFSY0024)the Key Research and Development Program in Sichuan Province of China(Grant No.2019YFG0050)the Natural Science Foundation of Guangxi Province of China(Grant No.AD19245021).
文摘Support vehicles are part of the main body of airport ground operations,and their scheduling efficiency directly impacts flight delays.A mathematical model is constructed and the responsiveness of support vehicles for current operational demands is proposed to study optimization algorithms for vehicle scheduling.The model is based on the constraint relationship of the initial operation time,time window,and gate position distribution,which gives an improvement to the ant colony algorithm(ACO).The impacts of the improved ACO as used for support vehicle optimization are compared and analyzed.The results show that the scheduling scheme of refueling trucks based on the improved ACO can reduce flight delays caused by refueling operations by 56.87%,indicating the improved ACO can improve support vehicle scheduling.Besides,the improved ACO can jump out of local optima,which can balance the working time of refueling trucks.This research optimizes the scheduling scheme of support vehicles under the existing conditions of airports,which has practical significance to fully utilize ground service resources,improve the efficiency of airport ground operations,and effectively reduce flight delays caused by ground service support.
基金funded by Deanship of Scientific Research at Najran University under the Research Groups Funding Program Grant Code(NU/RG/SERC/12/3)also by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2023R333)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Securing digital image data is a key concern in today’s information-driven society.Effective encryption techniques are required to protect sensitive image data,with the Substitution-box(S-box)often playing a pivotal role in many symmetric encryption systems.This study introduces an innovative approach to creating S-boxes for encryption algorithms.The proposed S-boxes are tested for validity and non-linearity by incorporating them into an image encryption scheme.The nonlinearity measure of the proposed S-boxes is 112.These qualities significantly enhance its resistance to common cryptographic attacks,ensuring high image data security.Furthermore,to assess the robustness of the S-boxes,an encryption system has also been proposed and the proposed S-boxes have been integrated into the designed encryption system.To validate the effectiveness of the proposed encryption system,a comprehensive security analysis including brute force attack and histogram analysis has been performed.In addition,to determine the level of security during the transmission and storage of digital content,the encryption system’s Number of Pixel Change Rate(NPCR),and Unified Averaged Changed Intensity(UACI)are calculated.The results indicate a 99.71%NPCR and 33.51%UACI.These results demonstrate that the proposed S-boxes offer a significant level of security for digital content throughout its transmission and storage.
文摘In this research,an Artificial Bee Colony(ABC)algorithm based Selective Harmonics Elimination(SHE)technique is used as a pulse generator in a reduced switch fifteen level inverter that receives input from a PV system.Pulse width modulation based on Selective Harmonics Elimination is mostly used to suppress lower-order harmonics.A high gain DC-DC-SEPIC converter keeps the photovoltaic(PV)panel’s output voltage constant.The Grey Wolf Optimization(GWO)filter removes far more Photovoltaic panel energy from the sunlight frame.To eliminate voltage harmonics,this unique inverter architecture employs a multi-carrier duty cycle,a high-frequency modulation approach.The proposed ABC harmonics elimination approach is compared to SHE strategies based on Particle Swarm Optimization(PSO)and Flower Pollination Algorithm(FPA).The suggested system’s performance is simulated and measured using the MATLAB simulation tool.The proposed ABC approach has a THD level of 4.86%,which is better than the PSO and FPA methods.
文摘This article presents an optimized approach of mathematical techniques in themedical domain by manoeuvring the phenomenon of ant colony optimization algorithm(also known as ACO).A complete graph of blood banks and a path that covers all the blood banks without repeating any link is required by applying the Travelling Salesman Problem(often TSP).The wide use promises to accelerate and offers the opportunity to cultivate health care,particularly in remote or unmerited environments by shrinking lab testing reversal times,empowering just-in-time lifesaving medical supply.
基金Deanship of Scientific Research at King Khalid University for funding this work through Large Group Research Project under Grant Number RGP2/249/44.
文摘Security testing is a critical concern for organizations worldwide due to the potential financial setbacks and damage to reputation caused by insecure software systems.One of the challenges in software security testing is test case prioritization,which aims to reduce redundancy in fault occurrences when executing test suites.By effectively applying test case prioritization,both the time and cost required for developing secure software can be reduced.This paper proposes a test case prioritization technique based on the Ant Colony Optimization(ACO)algorithm,a metaheuristic approach.The performance of the ACO-based technique is evaluated using the Average Percentage of Fault Detection(APFD)metric,comparing it with traditional techniques.It has been applied to a Mobile Payment Wallet application to validate the proposed approach.The results demonstrate that the proposed technique outperforms the traditional techniques in terms of the APFD metric.The ACO-based technique achieves an APFD of approximately 76%,two percent higher than the second-best optimal ordering technique.These findings suggest that metaheuristic-based prioritization techniques can effectively identify the best test cases,saving time and improving software security overall.
基金supported by the Langfang Science and Technology Plan Project(No.2018013151)from Hebei Petro China Central Hospital.
文摘Background:Recently,researchers have been attracted in identifying the crucial genes related to cancer,which plays important role in cancer diagnosis and treatment.However,in performing the cancer molecular subtype classification task from cancer gene expression data,it is challenging to obtain those significant genes due to the high dimensionality and high noise of data.Moreover,the existing methods always suffer from some issues such as premature convergence.Methods:To address those problems,we propose a new ant colony optimization(ACO)algorithm called DACO to classify the cancer gene expression datasets,identifying the essential genes of different diseases.In DACO,first,we propose the initial pheromone concentration based on the weight ranking vector to accelerate the convergence speed;then,a dynamic pheromone volatility factor is designed to prevent the algorithm from getting stuck in the local optimal solution;finally,the pheromone update rule in the Ant Colony System is employed to update the pheromone globally and locally.To demonstrate the performance of the proposed algorithm in classification,different existing approaches are compared with the proposed algorithm on eight high-dimensional cancer gene expression datasets.Results:The experiment results show that the proposed algorithm performs better than other effective methods in terms of classification accuracy and the number of feature sets.It can be used to address the classification problem effectively.Moreover,a renal cell carcinoma dataset is employed to reveal the biological significance of the proposed algorithm from a number of biological analyses.Conclusion:The results demonstrate that CAPS may play a crucial role in the occurrence and development of renal clear cell carcinoma.
基金supported in part by the National Key Research and Development Program of China under Grant 2021YFB2900604in part by the National Natural Science Foundation of China(NSFC)under Grant U22B2033,61975234,61875230。
文摘With the development of satellite communication,in order to solve the problems of shortage of on-board resources and refinement of delay requirements to improve the communication performance of satellite optical networks,this paper proposes a bee colony optimization algorithm for routing and wavelength assignment based on directional guidance(DBCO-RWA)in satellite optical networks.In D-BCORWA,directional guidance based on relative position and link load is defined,and then the link cost function in the path search stage is established based on the directional guidance factor.Finally,feasible solutions are expanded in the global optimization stage.The wavelength utilization,communication success probability,blocking rate,communication hops and convergence characteristic are simulated.The results show that the performance of the proposed algorithm is improved compared with existing algorithms.
基金The authors extend their appreciation to the Deputyship for Research and Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number 2022/01/22636.
文摘In the Internet of Things(IoT),the users have complex needs,and the Web Service Composition(WSC)was introduced to address these needs.The WSC’s main objective is to search for the optimal combination of web services in response to the user needs and the level of Quality of Services(QoS)constraints.The challenge of this problem is the huge number of web services that achieve similar functionality with different levels of QoS constraints.In this paper,we introduce an extension of our previous works on the Artificial Bee Colony(ABC)and Bat Algorithm(BA).A new hybrid algorithm was proposed between the ABC and BA to achieve a better tradeoff between local exploitation and global search.The bat agent is used to improve the solution of exhausted bees after a threshold(limits),and also an Elitist Strategy(ES)is added to BA to increase the convergence rate.The performance and convergence behavior of the proposed hybrid algorithm was tested using extensive comparative experiments with current state-ofthe-art nature-inspired algorithms on 12 benchmark datasets using three evaluation criteria(average fitness values,best fitness values,and execution time)that were measured for 30 different runs.These datasets are created from real-world datasets and artificially to form different scale sizes of WSC datasets.The results show that the proposed algorithm enhances the search performance and convergence rate on finding the near-optimal web services combination compared to competitors.TheWilcoxon signed-rank significant test is usedwhere the proposed algorithm results significantly differ fromother algorithms on 100%of datasets.
基金Ministry of Education in Saudi Arabia for funding this research work through the project number (IFPSAU-2021/01/17793)rch work through the project number (IFPSAU-2021/01/17793)。
文摘In recent years,cloud computing has provided a Software As A Service(SaaS)platform where the software can be reused and applied to fulfill compli-cated user demands according to specific Quality of Services(QoS)constraints.The user requirements are formulated as a workflow consisting of a set of tasks.However,many services may satisfy the functionality of each task;thus,searching for the composition of the optimal service while maximizing the QoS is formulated as an NP-hard problem.This work will introduce a hybrid Artificial Bee Colony(ABC)with a Cuckoo Search(CS)algorithm to untangle service composition problem.The ABC is a well-known metaheuristic algorithm that can be applied when dealing with different NP-hard problems with an outstanding record of performance.However,the ABC suffers from a slow convergence problem.Therefore,the CS is used to overcome the ABC’s limitations by allowing the abandoned bees to enhance their search and override the local optimum.The proposed hybrid algorithm has been tested on 19 datasets and then compared with two standard algorithms(ABC and CS)and three state-of-the-art swarm-based composition algorithms.In addition,extensive parameter study experiments were conducted to set up the proposed algorithm’s parameters.The results indicate that the proposed algorithm outperforms the standard algorithms in the three comparison criteria(bestfitness value,averagefitness value,and average execution time)overall datasets in 30 different runs.Furthermore,the proposed algorithm also exhibits better performance than the state–of–the–art algorithms in the three comparison criteria over 30 different runs.
基金National Natural Science Foundation of China(No.62241503)Natural Science Foundation of Shanghai,China(No.22ZR1401400)。
文摘A fusion algorithm is proposed to enhance the search speed of an ant colony system(ACS)for the global path planning and overcome the challenges of the local path planning in an unmanned aerial vehicle(UAV).The ACS search efficiency is enhanced by adopting a 16-direction 24-neighborhood search way,a safety grid search way,and an elite hybrid strategy to accelerate global convergence.Quadratic planning is performed using the moving average(MA)method.The fusion algorithm incorporates a dynamic window approach(DWA)to deal with the local path planning,sets a retracement mechanism,and adjusts the evaluation function accordingly.Experimental results in two environments demonstrate that the improved ant colony system(IACS)achieves superior planning efficiency.Additionally,the optimized dynamic window approach(ODWA)demonstrates its ability to handle multiple dynamic situations.Overall,the fusion optimization algorithm can accomplish the mixed path planning effectively.
基金supported by the National Natural Science Foundation of China(71771216).
文摘With the advancement of combat equipment technology and combat concepts,new requirements have been put forward for air defense operations during a group target attack.To achieve high-efficiency and lowloss defensive operations,a reasonable air defense weapon assignment strategy is a key step.In this paper,a multi-objective and multi-constraints weapon target assignment(WTA)model is established that aims to minimize the defensive resource loss,minimize total weapon consumption,and minimize the target residual effectiveness.An optimization framework of air defense weapon mission scheduling based on the multiobjective artificial bee colony(MOABC)algorithm is proposed.The solution for point-to-point saturated attack targets at different operational scales is achieved by encoding the nectar with real numbers.Simulations are performed for an imagined air defense scenario,where air defense weapons are saturated.The non-dominated solution sets are obtained by the MOABC algorithm to meet the operational demand.In the case where there are more weapons than targets,more diverse assignment schemes can be selected.According to the inverse generation distance(IGD)index,the convergence and diversity for the solutions of the non-dominated sorting genetic algorithm III(NSGA-III)algorithm and the MOABC algorithm are compared and analyzed.The results prove that the MOABC algorithm has better convergence and the solutions are more evenly distributed among the solution space.
文摘A relationship between lung transplant success and many features of recipients’/donors has long been studied.However,modeling a robust model of a potential impact on organ transplant success has proved challenging.In this study,a hybrid feature selection model was developed based on ant colony opti-mization(ACO)and k-nearest neighbor(kNN)classifier to investigate the rela-tionship between the most defining features of recipients/donors and lung transplant success using data from the United Network of Organ Sharing(UNOS).The proposed ACO-kNN approach explores the features space to identify the representative attributes and classify patients’functional status(i.e.,quality of life)after lung transplantation.The efficacy of the proposed model was verified using 3,684 records and 118 input features from the UNOS.The developed approach examined the reliability and validity of the lung allocation process.The results are promising regarding accuracy prediction to be 91.3%and low computational time,along with better decision capabilities,emphasizing the potential for automatic classification of the lung and other organs allocation pro-cesses.In addition,the proposed model recommends a new perspective on how medical experts and clinicians respond to uncertain and challenging lung alloca-tion strategies.Having such ACO-kNN model,a medical professional can sum-marize information through the proposed method and make decisions for the upcoming transplants to allocate the donor organ.
文摘My essay on Heart of Darkness explores Conrad’s dramatization of the voice and heart dynamic in the novella as a penetration of the deep-lying rationale of colonial operation and more generally the human condition at the turn of the 20th century.Three different forms of assumed centrality in the novella-capital,Eurocentrism,and the African interior as detrimental void symbolized by the heart of darkness form concentric circles that interconnect and work in concert as the core colonial ideology and produce a sense of incongruity and absurdity.The voice of this colonial ideological apparatus disguised as the universal subject of humanity and rationality finds its best expression in the eloquence of Kurtz,the specimen of culture seduced by its very opposite-the wilderness-right in the heart of darkness.“The horror”,as his final verdict of life as a European with real intent of living on his own terms at the turn of the 20th century,sums up the predicament of a civilization with too much culture and self-absorption for its own good.