For the mobile edge computing network consisting of multiple base stations and resourceconstrained user devices,network cost in terms of energy and delay will incur during task offloading from the user to the edge ser...For the mobile edge computing network consisting of multiple base stations and resourceconstrained user devices,network cost in terms of energy and delay will incur during task offloading from the user to the edge server.With the limitations imposed on transmission capacity,computing resource,and connection capacity,the per-slot online learning algorithm is first proposed to minimize the time-averaged network cost.In particular,by leveraging the theories of stochastic gradient descent and minimum cost maximum flow,the user association is jointly optimized with resource scheduling in each time slot.The theoretical analysis proves that the proposed approach can achieve asymptotic optimality without any prior knowledge of the network environment.Moreover,to alleviate the high network overhead incurred during user handover and task migration,a two-timescale optimization approach is proposed to avoid frequent changes in user association.With user association executed on a large timescale and the resource scheduling decided on the single time slot,the asymptotic optimality is preserved.Simulation results verify the effectiveness of the proposed online learning algorithms.展开更多
In this paper, deterministic and stochastic models for schistosomiasis involving four sub-populations are developed. Conditions are given under which system exhibits thresholds behavior. The disease-free equilibrium i...In this paper, deterministic and stochastic models for schistosomiasis involving four sub-populations are developed. Conditions are given under which system exhibits thresholds behavior. The disease-free equilibrium is globally asymptotically stable if R0 ?and the unique endemic equilibrium is globally asymptotically stable when R0 >?1. The populations are computationally simulated under various conditions. Comparisons are made between the deterministic and the stochastic model.展开更多
The“memory wall”of traditional von Neumann computing systems severely restricts the efficiency of data-intensive task execution,while in-memory computing(IMC)architecture is a promising approach to breaking the bott...The“memory wall”of traditional von Neumann computing systems severely restricts the efficiency of data-intensive task execution,while in-memory computing(IMC)architecture is a promising approach to breaking the bottleneck.Although variations and instability in ultra-scaled memory cells seriously degrade the calculation accuracy in IMC architectures,stochastic computing(SC)can compensate for these shortcomings due to its low sensitivity to cell disturbances.Furthermore,massive parallel computing can be processed to improve the speed and efficiency of the system.In this paper,by designing logic functions in NOR flash arrays,SC in IMC for the image edge detection is realized,demonstrating ultra-low computational complexity and power consumption(25.5 fJ/pixel at 2-bit sequence length).More impressively,the noise immunity is 6 times higher than that of the traditional binary method,showing good tolerances to cell variation and reliability degradation when implementing massive parallel computation in the array.展开更多
Peer-to-peer computation offloading has been a promising approach that enables resourcelimited Internet of Things(IoT)devices to offload their computation-intensive tasks to idle peer devices in proximity.Different fr...Peer-to-peer computation offloading has been a promising approach that enables resourcelimited Internet of Things(IoT)devices to offload their computation-intensive tasks to idle peer devices in proximity.Different from dedicated servers,the spare computation resources offered by peer devices are random and intermittent,which affects the offloading performance.The mutual interference caused by multiple simultaneous offloading requestors that share the same wireless channel further complicates the offloading decisions.In this work,we investigate the opportunistic peer-to-peer task offloading problem by jointly considering the stochastic task arrivals,dynamic interuser interference,and opportunistic availability of peer devices.Each requestor makes decisions on both local computation frequency and offloading transmission power to minimize its own expected long-term cost on tasks completion,which takes into consideration its energy consumption,task delay,and task loss due to buffer overflow.The dynamic decision process among multiple requestors is formulated as a stochastic game.By constructing the post-decision states,a decentralized online offloading algorithm is proposed,where each requestor as an independent learning agent learns to approach the optimal strategies with its local observations.Simulation results under different system parameter configurations demonstrate the proposed online algorithm achieves a better performance compared with some existing algorithms,especially in the scenarios with large task arrival probability or small helper availability probability.展开更多
Neuromorphic computing,inspired by the human brain,uses memristor devices for complex tasks.Recent studies show that self-organizing random nanowires can implement neuromorphic information processing,enabling data ana...Neuromorphic computing,inspired by the human brain,uses memristor devices for complex tasks.Recent studies show that self-organizing random nanowires can implement neuromorphic information processing,enabling data analysis.This paper presents a model based on these nanowire networks,with an improved conductance variation profile.We suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage pulses.The nanowire network layer generates dynamic behaviors for pulse voltages,allowing time series prediction analysis.Our experiment uses a double stochastic nanowire network architecture for processing multiple input signals,outperforming traditional reservoir computing in terms of fewer nodes,enriched dynamics and improved prediction accuracy.Experimental results confirm the high accuracy of this architecture on multiple real-time series datasets,making neuromorphic nanowire networks promising for physical implementation of reservoir computing.展开更多
Brain tumors come in various types,each with distinct characteristics and treatment approaches,making manual detection a time-consuming and potentially ambiguous process.Brain tumor detection is a valuable tool for ga...Brain tumors come in various types,each with distinct characteristics and treatment approaches,making manual detection a time-consuming and potentially ambiguous process.Brain tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment outcomes.Machine learning models have become key players in automating brain tumor detection.Gradient descent methods are the mainstream algorithms for solving machine learning models.In this paper,we propose a novel distributed proximal stochastic gradient descent approach to solve the L_(1)-Smooth Support Vector Machine(SVM)classifier for brain tumor detection.Firstly,the smooth hinge loss is introduced to be used as the loss function of SVM.It avoids the issue of nondifferentiability at the zero point encountered by the traditional hinge loss function during gradient descent optimization.Secondly,the L_(1) regularization method is employed to sparsify features and enhance the robustness of the model.Finally,adaptive proximal stochastic gradient descent(PGD)with momentum,and distributed adaptive PGDwithmomentum(DPGD)are proposed and applied to the L_(1)-Smooth SVM.Distributed computing is crucial in large-scale data analysis,with its value manifested in extending algorithms to distributed clusters,thus enabling more efficient processing ofmassive amounts of data.The DPGD algorithm leverages Spark,enabling full utilization of the computer’s multi-core resources.Due to its sparsity induced by L_(1) regularization on parameters,it exhibits significantly accelerated convergence speed.From the perspective of loss reduction,DPGD converges faster than PGD.The experimental results show that adaptive PGD withmomentumand its variants have achieved cutting-edge accuracy and efficiency in brain tumor detection.Frompre-trained models,both the PGD andDPGD outperform other models,boasting an accuracy of 95.21%.展开更多
Deep neural networks(DNN)are widely used in image recognition,image classification,and other fields.However,as the model size increases,the DNN hardware accelerators face the challenge of higher area overhead and ener...Deep neural networks(DNN)are widely used in image recognition,image classification,and other fields.However,as the model size increases,the DNN hardware accelerators face the challenge of higher area overhead and energy consumption.In recent years,stochastic computing(SC)has been considered a way to realize deep neural networks and reduce hardware consumption.A probabilistic compensation algorithm is proposed to solve the accuracy problem of stochastic calculation,and a fully parallel neural network accelerator based on a deterministic method is designed.The software simulation results show that the accuracy of the probability compensation algorithm on the CIFAR-10 data set is 95.32%,which is 14.98%higher than that of the traditional SC algorithm.The accuracy of the deterministic algorithm on the CIFAR-10 dataset is 95.06%,which is 14.72%higher than that of the traditional SC algorithm.The results of Very Large Scale Integration Circuit(VLSI)hardware tests show that the normalized energy efficiency of the fully parallel neural network accelerator based on the deterministic method is improved by 31%compared with the circuit based on binary computing.展开更多
Risk management often plays an important role in decision making un-der uncertainty.In quantitative risk management,assessing and optimizing risk metrics requires eficient computing techniques and reliable theoretical...Risk management often plays an important role in decision making un-der uncertainty.In quantitative risk management,assessing and optimizing risk metrics requires eficient computing techniques and reliable theoretical guarantees.In this pa-per,we introduce several topics on quantitative risk management and review some of the recent studies and advancements on the topics.We consider several risk metrics and study decision models that involve the metrics,with a main focus on the related com-puting techniques and theoretical properties.We show that stochastic optimization,as a powerful tool,can be leveraged to effectively address these problems.展开更多
Quanto options allow the buyer to exchange the foreign currency payoff into the domestic currency at a fixed exchange rate. We investigate quanto options with multiple underlying assets valued in different foreign cur...Quanto options allow the buyer to exchange the foreign currency payoff into the domestic currency at a fixed exchange rate. We investigate quanto options with multiple underlying assets valued in different foreign currencies each with a different strike price in the payoff function. We carry out a comparative performance analysis of different stochastic volatility (SV), stochastic correlation (SC), and stochastic exchange rate (SER) models to determine the best combination of these models for Monte Carlo (MC) simulation pricing. In addition, we test the performance of all model variants with constant correlation as a benchmark. We find that a combination of GARCH-Jump SV, Weibull SC, and Ornstein Uhlenbeck (OU) SER performs best. In addition, we analyze different discretization schemes and their results. In our simulations, the Milstein scheme yields the best balance between execution times and lower standard deviations of price estimates. Furthermore, we find that incorporating mean reversion into stochastic correlation and stochastic FX rate modeling is beneficial for MC simulation pricing. We improve the accuracy of our simulations by implementing antithetic variates variance reduction. Finally, we derive the correlation risk parameters Cora and Gora in our framework so that correlation hedging of quanto options can be performed.展开更多
With the rapid advancements in technology and science,optimization theory and algorithms have become increasingly important.A wide range of real-world problems is classified as optimization challenges,and meta-heurist...With the rapid advancements in technology and science,optimization theory and algorithms have become increasingly important.A wide range of real-world problems is classified as optimization challenges,and meta-heuristic algorithms have shown remarkable effectiveness in solving these challenges across diverse domains,such as machine learning,process control,and engineering design,showcasing their capability to address complex optimization problems.The Stochastic Fractal Search(SFS)algorithm is one of the most popular meta-heuristic optimization methods inspired by the fractal growth patterns of natural materials.Since its introduction by Hamid Salimi in 2015,SFS has garnered significant attention from researchers and has been applied to diverse optimization problems acrossmultiple disciplines.Its popularity can be attributed to several factors,including its simplicity,practical computational efficiency,ease of implementation,rapid convergence,high effectiveness,and ability to address singleandmulti-objective optimization problems,often outperforming other established algorithms.This review paper offers a comprehensive and detailed analysis of the SFS algorithm,covering its standard version,modifications,hybridization,and multi-objective implementations.The paper also examines several SFS applications across diverse domains,including power and energy systems,image processing,machine learning,wireless sensor networks,environmental modeling,economics and finance,and numerous engineering challenges.Furthermore,the paper critically evaluates the SFS algorithm’s performance,benchmarking its effectiveness against recently published meta-heuristic algorithms.In conclusion,the review highlights key findings and suggests potential directions for future developments and modifications of the SFS algorithm.展开更多
In 2022,Leukemia is the 13th most common diagnosis of cancer globally as per the source of the International Agency for Research on Cancer(IARC).Leukemia is still a threat and challenge for all regions because of 46.6...In 2022,Leukemia is the 13th most common diagnosis of cancer globally as per the source of the International Agency for Research on Cancer(IARC).Leukemia is still a threat and challenge for all regions because of 46.6%infection in Asia,and 22.1%and 14.7%infection rates in Europe and North America,respectively.To study the dynamics of Leukemia,the population of cells has been divided into three subpopulations of cells susceptible cells,infected cells,and immune cells.To investigate the memory effects and uncertainty in disease progression,leukemia modeling is developed using stochastic fractional delay differential equations(SFDDEs).The feasible properties of positivity,boundedness,and equilibria(i.e.,Leukemia Free Equilibrium(LFE)and Leukemia Present Equilibrium(LPE))of the model were studied rigorously.The local and global stabilities and sensitivity of the parameters around the equilibria under the assumption of reproduction numbers were investigated.To support the theoretical analysis of the model,the Grunwald Letnikov Nonstandard Finite Difference(GL-NSFD)method was used to simulate the results of each subpopulation with memory effect.Also,the positivity and boundedness of the proposed method were studied.Our results show how different methods can help control the cell population and give useful advice to decision-makers on ways to lower leukemia rates in communities.展开更多
This study investigates the transmission dynamics of conjunctivitis using stochastic delay differential equations(SDDEs).A delayed stochastic model is formulated by dividing the population into five distinct compartme...This study investigates the transmission dynamics of conjunctivitis using stochastic delay differential equations(SDDEs).A delayed stochastic model is formulated by dividing the population into five distinct compartments:susceptible,exposed,infected,environmental irritants,and recovered individuals.The model undergoes thorough analytical examination,addressing key dynamical properties including positivity,boundedness,existence,and uniqueness of solutions.Local and global stability around the equilibrium points is studied with respect to the basic reproduction number.The existence of a unique global positive solution for the stochastic delayed model is established.In addition,a stochastic nonstandard finite difference scheme is developed,which is shown to be dynamically consistent and convergent toward the equilibrium states.The scheme preserves the essential qualitative features of the model and demonstrates improved performance when compared to existing numerical methods.Finally,the impact of time delays and stochastic fluctuations on the susceptible and infected populations is analyzed.展开更多
Streptococcus suis(S.suis)is a major disease impacting pig farming globally.It can also be transferred to humans by eating raw pork.A comprehensive study was recently carried out to determine the indices throughmultip...Streptococcus suis(S.suis)is a major disease impacting pig farming globally.It can also be transferred to humans by eating raw pork.A comprehensive study was recently carried out to determine the indices throughmultiple geographic regions in China.Methods:The well-posed theorems were employed to conduct a thorough analysis of the model’s feasible features,including positivity,boundedness equilibria,reproduction number,and parameter sensitivity.Stochastic Euler,Runge Kutta,and EulerMaruyama are some of the numerical techniques used to replicate the behavior of the streptococcus suis infection in the pig population.However,the dynamic qualities of the suggested model cannot be restored using these techniques.Results:For the stochastic delay differential equations of the model,the non-standard finite difference approach in the sense of stochasticity is developed to avoid several problems such as negativity,unboundedness,inconsistency,and instability of the findings.Results from traditional stochastic methods either converge conditionally or diverge over time.The stochastic non-negative step size convergence nonstandard finite difference(NSFD)method unconditionally converges to the model’s true states.Conclusions:This study improves our understanding of the dynamics of streptococcus suis infection using versions of stochastic with delay approaches and opens up new avenues for the study of cognitive processes and neuronal analysis.Theplotted interaction behaviour and new solution comparison profiles.展开更多
To achieve high quality of service （QoS） on computational grids, the QoS-aware job scheduling is investigated for a hierarchical decentralized grid architecture that consists of multilevel schedulers. An integrated ...To achieve high quality of service （QoS） on computational grids, the QoS-aware job scheduling is investigated for a hierarchical decentralized grid architecture that consists of multilevel schedulers. An integrated QoS-aware job dispatching policy is proposed, which correlates priorities of incoming jobs used for job selecting at the local scheduler of the grid node with the job dispatching policies at the global scheduler for computational grids. The stochastic high-level Petri net （SHLPN） model of a two-level hierarchy computational grid architecture is presented, and a model refinement is made to reduce the complexity of the model solution. A performance analysis technique based on the SHLPN is proposed to investigate the QoS-aware job scheduling policy. Numerical results show that the QoS-aware job dispatching policy outperforms the QoS-unaware job dispatching policy in balancing the high-priority jobs, and thus enables priority-based QoS.展开更多
基金the National Natural Science Foundation of China(61971066,61941114)the Beijing Natural Science Foundation(No.L182038)National Youth Top-notch Talent Support Program.
文摘For the mobile edge computing network consisting of multiple base stations and resourceconstrained user devices,network cost in terms of energy and delay will incur during task offloading from the user to the edge server.With the limitations imposed on transmission capacity,computing resource,and connection capacity,the per-slot online learning algorithm is first proposed to minimize the time-averaged network cost.In particular,by leveraging the theories of stochastic gradient descent and minimum cost maximum flow,the user association is jointly optimized with resource scheduling in each time slot.The theoretical analysis proves that the proposed approach can achieve asymptotic optimality without any prior knowledge of the network environment.Moreover,to alleviate the high network overhead incurred during user handover and task migration,a two-timescale optimization approach is proposed to avoid frequent changes in user association.With user association executed on a large timescale and the resource scheduling decided on the single time slot,the asymptotic optimality is preserved.Simulation results verify the effectiveness of the proposed online learning algorithms.
文摘In this paper, deterministic and stochastic models for schistosomiasis involving four sub-populations are developed. Conditions are given under which system exhibits thresholds behavior. The disease-free equilibrium is globally asymptotically stable if R0 ?and the unique endemic equilibrium is globally asymptotically stable when R0 >?1. The populations are computationally simulated under various conditions. Comparisons are made between the deterministic and the stochastic model.
基金supported by the National Natural Science Foundation of China(Nos.62034006,91964105,61874068)the China Key Research and Development Program(No.2016YFA0201802)+1 种基金the Natural Science Foundation of Shandong Province(No.ZR2020JQ28)Program of Qilu Young Scholars of Shandong University。
文摘The“memory wall”of traditional von Neumann computing systems severely restricts the efficiency of data-intensive task execution,while in-memory computing(IMC)architecture is a promising approach to breaking the bottleneck.Although variations and instability in ultra-scaled memory cells seriously degrade the calculation accuracy in IMC architectures,stochastic computing(SC)can compensate for these shortcomings due to its low sensitivity to cell disturbances.Furthermore,massive parallel computing can be processed to improve the speed and efficiency of the system.In this paper,by designing logic functions in NOR flash arrays,SC in IMC for the image edge detection is realized,demonstrating ultra-low computational complexity and power consumption(25.5 fJ/pixel at 2-bit sequence length).More impressively,the noise immunity is 6 times higher than that of the traditional binary method,showing good tolerances to cell variation and reliability degradation when implementing massive parallel computation in the array.
基金supported by National Natural Science Foundation of China (No. 62101601)
文摘Peer-to-peer computation offloading has been a promising approach that enables resourcelimited Internet of Things(IoT)devices to offload their computation-intensive tasks to idle peer devices in proximity.Different from dedicated servers,the spare computation resources offered by peer devices are random and intermittent,which affects the offloading performance.The mutual interference caused by multiple simultaneous offloading requestors that share the same wireless channel further complicates the offloading decisions.In this work,we investigate the opportunistic peer-to-peer task offloading problem by jointly considering the stochastic task arrivals,dynamic interuser interference,and opportunistic availability of peer devices.Each requestor makes decisions on both local computation frequency and offloading transmission power to minimize its own expected long-term cost on tasks completion,which takes into consideration its energy consumption,task delay,and task loss due to buffer overflow.The dynamic decision process among multiple requestors is formulated as a stochastic game.By constructing the post-decision states,a decentralized online offloading algorithm is proposed,where each requestor as an independent learning agent learns to approach the optimal strategies with its local observations.Simulation results under different system parameter configurations demonstrate the proposed online algorithm achieves a better performance compared with some existing algorithms,especially in the scenarios with large task arrival probability or small helper availability probability.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. U20A20227,62076208, and 62076207)Chongqing Talent Plan “Contract System” Project (Grant No. CQYC20210302257)+3 种基金National Key Laboratory of Smart Vehicle Safety Technology Open Fund Project (Grant No. IVSTSKL-202309)the Chongqing Technology Innovation and Application Development Special Major Project (Grant No. CSTB2023TIAD-STX0020)College of Artificial Intelligence, Southwest UniversityState Key Laboratory of Intelligent Vehicle Safety Technology
文摘Neuromorphic computing,inspired by the human brain,uses memristor devices for complex tasks.Recent studies show that self-organizing random nanowires can implement neuromorphic information processing,enabling data analysis.This paper presents a model based on these nanowire networks,with an improved conductance variation profile.We suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage pulses.The nanowire network layer generates dynamic behaviors for pulse voltages,allowing time series prediction analysis.Our experiment uses a double stochastic nanowire network architecture for processing multiple input signals,outperforming traditional reservoir computing in terms of fewer nodes,enriched dynamics and improved prediction accuracy.Experimental results confirm the high accuracy of this architecture on multiple real-time series datasets,making neuromorphic nanowire networks promising for physical implementation of reservoir computing.
基金the Natural Science Foundation of Ningxia Province(No.2021AAC03230).
文摘Brain tumors come in various types,each with distinct characteristics and treatment approaches,making manual detection a time-consuming and potentially ambiguous process.Brain tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment outcomes.Machine learning models have become key players in automating brain tumor detection.Gradient descent methods are the mainstream algorithms for solving machine learning models.In this paper,we propose a novel distributed proximal stochastic gradient descent approach to solve the L_(1)-Smooth Support Vector Machine(SVM)classifier for brain tumor detection.Firstly,the smooth hinge loss is introduced to be used as the loss function of SVM.It avoids the issue of nondifferentiability at the zero point encountered by the traditional hinge loss function during gradient descent optimization.Secondly,the L_(1) regularization method is employed to sparsify features and enhance the robustness of the model.Finally,adaptive proximal stochastic gradient descent(PGD)with momentum,and distributed adaptive PGDwithmomentum(DPGD)are proposed and applied to the L_(1)-Smooth SVM.Distributed computing is crucial in large-scale data analysis,with its value manifested in extending algorithms to distributed clusters,thus enabling more efficient processing ofmassive amounts of data.The DPGD algorithm leverages Spark,enabling full utilization of the computer’s multi-core resources.Due to its sparsity induced by L_(1) regularization on parameters,it exhibits significantly accelerated convergence speed.From the perspective of loss reduction,DPGD converges faster than PGD.The experimental results show that adaptive PGD withmomentumand its variants have achieved cutting-edge accuracy and efficiency in brain tumor detection.Frompre-trained models,both the PGD andDPGD outperform other models,boasting an accuracy of 95.21%.
文摘Deep neural networks(DNN)are widely used in image recognition,image classification,and other fields.However,as the model size increases,the DNN hardware accelerators face the challenge of higher area overhead and energy consumption.In recent years,stochastic computing(SC)has been considered a way to realize deep neural networks and reduce hardware consumption.A probabilistic compensation algorithm is proposed to solve the accuracy problem of stochastic calculation,and a fully parallel neural network accelerator based on a deterministic method is designed.The software simulation results show that the accuracy of the probability compensation algorithm on the CIFAR-10 data set is 95.32%,which is 14.98%higher than that of the traditional SC algorithm.The accuracy of the deterministic algorithm on the CIFAR-10 dataset is 95.06%,which is 14.72%higher than that of the traditional SC algorithm.The results of Very Large Scale Integration Circuit(VLSI)hardware tests show that the normalized energy efficiency of the fully parallel neural network accelerator based on the deterministic method is improved by 31%compared with the circuit based on binary computing.
文摘Risk management often plays an important role in decision making un-der uncertainty.In quantitative risk management,assessing and optimizing risk metrics requires eficient computing techniques and reliable theoretical guarantees.In this pa-per,we introduce several topics on quantitative risk management and review some of the recent studies and advancements on the topics.We consider several risk metrics and study decision models that involve the metrics,with a main focus on the related com-puting techniques and theoretical properties.We show that stochastic optimization,as a powerful tool,can be leveraged to effectively address these problems.
文摘Quanto options allow the buyer to exchange the foreign currency payoff into the domestic currency at a fixed exchange rate. We investigate quanto options with multiple underlying assets valued in different foreign currencies each with a different strike price in the payoff function. We carry out a comparative performance analysis of different stochastic volatility (SV), stochastic correlation (SC), and stochastic exchange rate (SER) models to determine the best combination of these models for Monte Carlo (MC) simulation pricing. In addition, we test the performance of all model variants with constant correlation as a benchmark. We find that a combination of GARCH-Jump SV, Weibull SC, and Ornstein Uhlenbeck (OU) SER performs best. In addition, we analyze different discretization schemes and their results. In our simulations, the Milstein scheme yields the best balance between execution times and lower standard deviations of price estimates. Furthermore, we find that incorporating mean reversion into stochastic correlation and stochastic FX rate modeling is beneficial for MC simulation pricing. We improve the accuracy of our simulations by implementing antithetic variates variance reduction. Finally, we derive the correlation risk parameters Cora and Gora in our framework so that correlation hedging of quanto options can be performed.
基金supported by Prince Sattam bin Abdulaziz University for funding this research work through the project number(2024/RV/06).
文摘With the rapid advancements in technology and science,optimization theory and algorithms have become increasingly important.A wide range of real-world problems is classified as optimization challenges,and meta-heuristic algorithms have shown remarkable effectiveness in solving these challenges across diverse domains,such as machine learning,process control,and engineering design,showcasing their capability to address complex optimization problems.The Stochastic Fractal Search(SFS)algorithm is one of the most popular meta-heuristic optimization methods inspired by the fractal growth patterns of natural materials.Since its introduction by Hamid Salimi in 2015,SFS has garnered significant attention from researchers and has been applied to diverse optimization problems acrossmultiple disciplines.Its popularity can be attributed to several factors,including its simplicity,practical computational efficiency,ease of implementation,rapid convergence,high effectiveness,and ability to address singleandmulti-objective optimization problems,often outperforming other established algorithms.This review paper offers a comprehensive and detailed analysis of the SFS algorithm,covering its standard version,modifications,hybridization,and multi-objective implementations.The paper also examines several SFS applications across diverse domains,including power and energy systems,image processing,machine learning,wireless sensor networks,environmental modeling,economics and finance,and numerous engineering challenges.Furthermore,the paper critically evaluates the SFS algorithm’s performance,benchmarking its effectiveness against recently published meta-heuristic algorithms.In conclusion,the review highlights key findings and suggests potential directions for future developments and modifications of the SFS algorithm.
基金supported by the Fundacao para a Ciencia e Tecnologia,FCT,under the project https://doi.org/10.54499/UIDB/04674/2020(accessed on 1 January 2025).
文摘In 2022,Leukemia is the 13th most common diagnosis of cancer globally as per the source of the International Agency for Research on Cancer(IARC).Leukemia is still a threat and challenge for all regions because of 46.6%infection in Asia,and 22.1%and 14.7%infection rates in Europe and North America,respectively.To study the dynamics of Leukemia,the population of cells has been divided into three subpopulations of cells susceptible cells,infected cells,and immune cells.To investigate the memory effects and uncertainty in disease progression,leukemia modeling is developed using stochastic fractional delay differential equations(SFDDEs).The feasible properties of positivity,boundedness,and equilibria(i.e.,Leukemia Free Equilibrium(LFE)and Leukemia Present Equilibrium(LPE))of the model were studied rigorously.The local and global stabilities and sensitivity of the parameters around the equilibria under the assumption of reproduction numbers were investigated.To support the theoretical analysis of the model,the Grunwald Letnikov Nonstandard Finite Difference(GL-NSFD)method was used to simulate the results of each subpopulation with memory effect.Also,the positivity and boundedness of the proposed method were studied.Our results show how different methods can help control the cell population and give useful advice to decision-makers on ways to lower leukemia rates in communities.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R899)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabiasupported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia(KFU252831)。
文摘This study investigates the transmission dynamics of conjunctivitis using stochastic delay differential equations(SDDEs).A delayed stochastic model is formulated by dividing the population into five distinct compartments:susceptible,exposed,infected,environmental irritants,and recovered individuals.The model undergoes thorough analytical examination,addressing key dynamical properties including positivity,boundedness,existence,and uniqueness of solutions.Local and global stability around the equilibrium points is studied with respect to the basic reproduction number.The existence of a unique global positive solution for the stochastic delayed model is established.In addition,a stochastic nonstandard finite difference scheme is developed,which is shown to be dynamically consistent and convergent toward the equilibrium states.The scheme preserves the essential qualitative features of the model and demonstrates improved performance when compared to existing numerical methods.Finally,the impact of time delays and stochastic fluctuations on the susceptible and infected populations is analyzed.
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia[KFU250259].
文摘Streptococcus suis(S.suis)is a major disease impacting pig farming globally.It can also be transferred to humans by eating raw pork.A comprehensive study was recently carried out to determine the indices throughmultiple geographic regions in China.Methods:The well-posed theorems were employed to conduct a thorough analysis of the model’s feasible features,including positivity,boundedness equilibria,reproduction number,and parameter sensitivity.Stochastic Euler,Runge Kutta,and EulerMaruyama are some of the numerical techniques used to replicate the behavior of the streptococcus suis infection in the pig population.However,the dynamic qualities of the suggested model cannot be restored using these techniques.Results:For the stochastic delay differential equations of the model,the non-standard finite difference approach in the sense of stochasticity is developed to avoid several problems such as negativity,unboundedness,inconsistency,and instability of the findings.Results from traditional stochastic methods either converge conditionally or diverge over time.The stochastic non-negative step size convergence nonstandard finite difference(NSFD)method unconditionally converges to the model’s true states.Conclusions:This study improves our understanding of the dynamics of streptococcus suis infection using versions of stochastic with delay approaches and opens up new avenues for the study of cognitive processes and neuronal analysis.Theplotted interaction behaviour and new solution comparison profiles.
基金The National Natural Science Foundation of China(No60673054,90412012)
文摘To achieve high quality of service （QoS） on computational grids, the QoS-aware job scheduling is investigated for a hierarchical decentralized grid architecture that consists of multilevel schedulers. An integrated QoS-aware job dispatching policy is proposed, which correlates priorities of incoming jobs used for job selecting at the local scheduler of the grid node with the job dispatching policies at the global scheduler for computational grids. The stochastic high-level Petri net （SHLPN） model of a two-level hierarchy computational grid architecture is presented, and a model refinement is made to reduce the complexity of the model solution. A performance analysis technique based on the SHLPN is proposed to investigate the QoS-aware job scheduling policy. Numerical results show that the QoS-aware job dispatching policy outperforms the QoS-unaware job dispatching policy in balancing the high-priority jobs, and thus enables priority-based QoS.
