The equilibrium optimizer(EO)represents a new,physics-inspired metaheuristic optimization approach that draws inspiration from the principles governing the control of volume-based mixing to achieve dynamic mass equili...The equilibrium optimizer(EO)represents a new,physics-inspired metaheuristic optimization approach that draws inspiration from the principles governing the control of volume-based mixing to achieve dynamic mass equilibrium.Despite its innovative foundation,the EO exhibits certain limitations,including imbalances between exploration and exploitation,the tendency to local optima,and the susceptibility to loss of population diversity.To alleviate these drawbacks,this paper introduces an improved EO that adopts three strategies:adaptive inertia weight,Cauchy mutation,and adaptive sine cosine mechanism,called SCEO.Firstly,a new update formula is conceived by incorporating an adaptive inertia weight to reach an appropriate balance between exploration and exploitation.Next,an adaptive sine cosine mechanism is embedded to boost the global exploratory capacity.Finally,the Cauchy mutation is utilized to prevent the loss of population diversity during searching.To validate the efficacy of the proposed SCEO,a comprehensive evaluation is conducted on 15 classical benchmark functions and the CEC2017 test suite.The outcomes are subsequently benchmarked against both the conventional EO,its variants,and other cutting-edge metaheuristic techniques.The comparisons reveal that the SCEO method provides significantly superior results against the standard EO and other competitors.In addition,the developed SCEO is implemented to deal with a mobile robot path planning(MRPP)task,and compared to some classical metaheuristic approaches.The analysis results demonstrate that the SCEO approach provides the best performance and is a prospective tool for MRPP.展开更多
Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by emotions.The number of features acquired with acoustic analysis is ext...Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by emotions.The number of features acquired with acoustic analysis is extremely high,so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition system.The proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum accuracy.First,we use the information gain and Fisher Score to sort the features extracted from signals.Then,we employ a multi-objective ranking method to evaluate these features and assign different importance to them.Features with high rankings have a large probability of being selected.Finally,we propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection,which can improve the diversity of solutions and avoid falling into local traps.Using random forest and K-nearest neighbor classifiers,four English speech emotion datasets are employed to test the proposed algorithm(MBEO)as well as other multi-objective emotion identification techniques.The results illustrate that it performs well in inverted generational distance,hypervolume,Pareto solutions,and execution time,and MBEO is appropriate for high-dimensional English SER.展开更多
More devices in the Intelligent Internet of Things(AIoT)result in an increased number of tasks that require low latency and real-time responsiveness,leading to an increased demand for computational resources.Cloud com...More devices in the Intelligent Internet of Things(AIoT)result in an increased number of tasks that require low latency and real-time responsiveness,leading to an increased demand for computational resources.Cloud computing’s low-latency performance issues in AIoT scenarios have led researchers to explore fog computing as a complementary extension.However,the effective allocation of resources for task execution within fog environments,characterized by limitations and heterogeneity in computational resources,remains a formidable challenge.To tackle this challenge,in this study,we integrate fog computing and cloud computing.We begin by establishing a fog-cloud environment framework,followed by the formulation of a mathematical model for task scheduling.Lastly,we introduce an enhanced hybrid Equilibrium Optimizer(EHEO)tailored for AIoT task scheduling.The overarching objective is to decrease both the makespan and energy consumption of the fog-cloud system while accounting for task deadlines.The proposed EHEO method undergoes a thorough evaluation against multiple benchmark algorithms,encompassing metrics likemakespan,total energy consumption,success rate,and average waiting time.Comprehensive experimental results unequivocally demonstrate the superior performance of EHEO across all assessed metrics.Notably,in the most favorable conditions,EHEO significantly diminishes both the makespan and energy consumption by approximately 50%and 35.5%,respectively,compared to the secondbest performing approach,which affirms its efficacy in advancing the efficiency of AIoT task scheduling within fog-cloud networks.展开更多
The liquid cooling system(LCS)of fuel cells is challenged by significant time delays,model uncertainties,pump and fan coupling,and frequent disturbances,leading to overshoot and control oscillations that degrade tempe...The liquid cooling system(LCS)of fuel cells is challenged by significant time delays,model uncertainties,pump and fan coupling,and frequent disturbances,leading to overshoot and control oscillations that degrade temperature regulation performance.To address these challenges,we propose a composite control scheme combining fuzzy logic and a variable-gain generalized supertwisting algorithm(VG-GSTA).Firstly,a one-dimensional(1D)fuzzy logic controler(FLC)for the pump ensures stable coolant flow,while a two-dimensional(2D)FLC for the fan regulates the stack temperature near the reference value.The VG-GSTA is then introduced to eliminate steady-state errors,offering resistance to disturbances and minimizing control oscillations.The equilibrium optimizer is used to fine-tune VG-GSTA parameters.Co-simulation verifies the effectiveness of our method,demonstrating its advantages in terms of disturbance immunity,overshoot suppression,tracking accuracy and response speed.展开更多
The Equilibrium Optimizer(EO),Grey Wolf Optimizer(GWO),and Whale Optimizer(WO)algorithms are being recently developed for engineering optimization problems.In this paper,the EO,GWO,and WO algorithms are applied indivi...The Equilibrium Optimizer(EO),Grey Wolf Optimizer(GWO),and Whale Optimizer(WO)algorithms are being recently developed for engineering optimization problems.In this paper,the EO,GWO,and WO algorithms are applied individually for a brushless direct current(BLDC)design optimization problem.The EO algorithm is inspired by the models utilized to find the system’s dynamic state and equilibrium state.The GWO and WO algorithms are inspired by the hunting behavior of the wolf and the whale,respectively.The primary purpose of any optimization technique is to find the optimal configuration by maximizing motor efficiency and/or minimizing the total mass.Therefore,two objective functions are being used to achieve these objectives.The first refers to a design with high power output and efficiency.The second is a constraint imposed by the reality that the motor is built into the wheel of the vehicle and,therefore,a lightweight is needed.The EO,GWO,and WOA algorithms are then utilized to optimize the BLDC motor’s design variables to minimize the motor’s total mass or maximize the motor efficiency by simultaneously satisfying the six inequality constraints.The simulation is carried out using MATLAB simulation software,and the simulation results prove the dominance of the proposed algorithms.This paper also suggests an efficient method from the proposed three methods for the BLDC motor design optimization problem.展开更多
With the rapid development of communication technology,the problem of antenna array optimization plays a crucial role.Among many types of antennas,line antenna arrays(LAA)are the most commonly applied,but the side lob...With the rapid development of communication technology,the problem of antenna array optimization plays a crucial role.Among many types of antennas,line antenna arrays(LAA)are the most commonly applied,but the side lobe level(SLL)reduction is still a challenging problem.In the radiation process of the linear antenna array,the high side lobe level will interfere with the intensity of the antenna target radiation direction.Many conventional methods are ineffective in obtaining the maximumside lobe level in synthesis,and this paper proposed a quantum equilibrium optimizer(QEO)algorithm for line antenna arrays.Firstly,the linear antenna array model consists of an array element arrangement.Array factor(AF)can be expressed as the combination of array excitation amplitude and position in array space.Then,inspired by the powerful computing power of quantum computing,an improved quantum equilibrium optimizer combining quantum coding and quantum rotation gate strategy is proposed.Finally,the proposed quantum equilibrium optimizer is used to optimize the excitation amplitude of the array elements in the linear antenna array model by numerical simulation to minimize the interference of the side lobe level to the main lobe radiation.Six differentmetaheuristic algorithms are used to optimize the excitation amplitude in three different arrays of line antenna arrays,the experimental results indicated that the quantum equilibrium optimizer is more advantageous in obtaining the maximum side lobe level reduction.Compared with other metaheuristic optimization algorithms,the quantum equilibrium optimizer has advantages in terms of convergence speed and accuracy.展开更多
For bistatic multiple-input multiple-output(MIMO)radar,this paper presents a robust and direction finding method in strong impulse noise environment.By means of a new lower order covariance,the method is effective in ...For bistatic multiple-input multiple-output(MIMO)radar,this paper presents a robust and direction finding method in strong impulse noise environment.By means of a new lower order covariance,the method is effective in suppressing impulse noise and achieving superior direction finding performance using the maximum likelihood(ML)estimation method.A quantum equilibrium optimizer algorithm(QEOA)is devised to resolve the corresponding objective function for efficient and accurate direc-tion finding.The results of simulation reveal the capability of the presented method in success rate and root mean square error over existing direction-finding methods in different application situations,e.g.,locating coherent signal sources with very few snapshots in strong impulse noise.Other than that,the Cramér-Rao bound(CRB)under impulse noise environment has been drawn to test the capability of the presented method.展开更多
Supply chains and other complex systems can be effectively managed and optimised with the help of optimal control techniques.Optimal control,as used in supply chain management,is the process of using mathematical opti...Supply chains and other complex systems can be effectively managed and optimised with the help of optimal control techniques.Optimal control,as used in supply chain management,is the process of using mathematical optimisation techniques to identify the best course of action for controlling a given objective function over time.Modeling the supply chain’s dynamics,which include elements like production rates,inventory levels,demand trends,and transportation constraints,is the best control strategy when applied to a supply chain.In this study,we have considered that production rate is an unknown function of time,which is a controlling function.The demand for the product is taken as a function of price and time.The emission of carbon is taken as a linear function of the production rate of the system.To solve the suggested supply chain system,we have used an optimal control approach for determining the unknown production rate.To find the optimal values of the objective function as well as the decision variables,we have used different meta-heuristic algorithms and compared their results.It is observed that the equilibrium optimizer algorithm performed better than other algorithms used.Finally,a sensitivity analysis is performed,which is presented graphically in order to choose the best course of action.展开更多
The paper discusses how to reach the equilibrium and optimization GI during the period of economic transformation. The market economy might not work because of its mechanism flaws, based on the assumption that the gov...The paper discusses how to reach the equilibrium and optimization GI during the period of economic transformation. The market economy might not work because of its mechanism flaws, based on the assumption that the government is the supplier and the market economy is the demander Of GI, there is an equilibrium and optimization issue. The theory suggests that GI could reach equilibrium through adjusting the government revenue, thus leads to the result of functional complement between the market economy and the GI, and the optimum economic efficiency.展开更多
This paper deals with the problem of nonconstant harvesting of prey in a ratio-dependent predator-prey system incorporating a constant prey refuge. Here we use the reasonable catch-rate function instead of usual catch...This paper deals with the problem of nonconstant harvesting of prey in a ratio-dependent predator-prey system incorporating a constant prey refuge. Here we use the reasonable catch-rate function instead of usual catch-per-unit-effort hypothesis. The existence, as well as the stability of possible equilibria, is carried out. Bionomic equilibrium of the system is determined and optimal harvest policy is studied with the help of Pontryagin＇s maximum principle. The key results developed in this paper are illustrated using numer- ical simulations. Our results indicate that dynamic behavior of the system very much depends on the prey refuge parameter and increasing amount of refuge could increase prey density and may lead to the extinction of predator population density.展开更多
基金support from the National Natural Science Foundation of China[Grant Nos.61461053,61461054,and 61072079]Yunnan Provincial Education Department Scientific Research Fund Project[2022Y008].
文摘The equilibrium optimizer(EO)represents a new,physics-inspired metaheuristic optimization approach that draws inspiration from the principles governing the control of volume-based mixing to achieve dynamic mass equilibrium.Despite its innovative foundation,the EO exhibits certain limitations,including imbalances between exploration and exploitation,the tendency to local optima,and the susceptibility to loss of population diversity.To alleviate these drawbacks,this paper introduces an improved EO that adopts three strategies:adaptive inertia weight,Cauchy mutation,and adaptive sine cosine mechanism,called SCEO.Firstly,a new update formula is conceived by incorporating an adaptive inertia weight to reach an appropriate balance between exploration and exploitation.Next,an adaptive sine cosine mechanism is embedded to boost the global exploratory capacity.Finally,the Cauchy mutation is utilized to prevent the loss of population diversity during searching.To validate the efficacy of the proposed SCEO,a comprehensive evaluation is conducted on 15 classical benchmark functions and the CEC2017 test suite.The outcomes are subsequently benchmarked against both the conventional EO,its variants,and other cutting-edge metaheuristic techniques.The comparisons reveal that the SCEO method provides significantly superior results against the standard EO and other competitors.In addition,the developed SCEO is implemented to deal with a mobile robot path planning(MRPP)task,and compared to some classical metaheuristic approaches.The analysis results demonstrate that the SCEO approach provides the best performance and is a prospective tool for MRPP.
文摘Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by emotions.The number of features acquired with acoustic analysis is extremely high,so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition system.The proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum accuracy.First,we use the information gain and Fisher Score to sort the features extracted from signals.Then,we employ a multi-objective ranking method to evaluate these features and assign different importance to them.Features with high rankings have a large probability of being selected.Finally,we propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection,which can improve the diversity of solutions and avoid falling into local traps.Using random forest and K-nearest neighbor classifiers,four English speech emotion datasets are employed to test the proposed algorithm(MBEO)as well as other multi-objective emotion identification techniques.The results illustrate that it performs well in inverted generational distance,hypervolume,Pareto solutions,and execution time,and MBEO is appropriate for high-dimensional English SER.
基金in part by the Hubei Natural Science and Research Project under Grant 2020418in part by the 2021 Light of Taihu Science and Technology Projectin part by the 2022 Wuxi Science and Technology Innovation and Entrepreneurship Program.
文摘More devices in the Intelligent Internet of Things(AIoT)result in an increased number of tasks that require low latency and real-time responsiveness,leading to an increased demand for computational resources.Cloud computing’s low-latency performance issues in AIoT scenarios have led researchers to explore fog computing as a complementary extension.However,the effective allocation of resources for task execution within fog environments,characterized by limitations and heterogeneity in computational resources,remains a formidable challenge.To tackle this challenge,in this study,we integrate fog computing and cloud computing.We begin by establishing a fog-cloud environment framework,followed by the formulation of a mathematical model for task scheduling.Lastly,we introduce an enhanced hybrid Equilibrium Optimizer(EHEO)tailored for AIoT task scheduling.The overarching objective is to decrease both the makespan and energy consumption of the fog-cloud system while accounting for task deadlines.The proposed EHEO method undergoes a thorough evaluation against multiple benchmark algorithms,encompassing metrics likemakespan,total energy consumption,success rate,and average waiting time.Comprehensive experimental results unequivocally demonstrate the superior performance of EHEO across all assessed metrics.Notably,in the most favorable conditions,EHEO significantly diminishes both the makespan and energy consumption by approximately 50%and 35.5%,respectively,compared to the secondbest performing approach,which affirms its efficacy in advancing the efficiency of AIoT task scheduling within fog-cloud networks.
基金Supported by the Major Science and Technology Project of Jilin Province(20220301010GX)the International Scientific and Technological Cooperation(20240402071GH).
文摘The liquid cooling system(LCS)of fuel cells is challenged by significant time delays,model uncertainties,pump and fan coupling,and frequent disturbances,leading to overshoot and control oscillations that degrade temperature regulation performance.To address these challenges,we propose a composite control scheme combining fuzzy logic and a variable-gain generalized supertwisting algorithm(VG-GSTA).Firstly,a one-dimensional(1D)fuzzy logic controler(FLC)for the pump ensures stable coolant flow,while a two-dimensional(2D)FLC for the fan regulates the stack temperature near the reference value.The VG-GSTA is then introduced to eliminate steady-state errors,offering resistance to disturbances and minimizing control oscillations.The equilibrium optimizer is used to fine-tune VG-GSTA parameters.Co-simulation verifies the effectiveness of our method,demonstrating its advantages in terms of disturbance immunity,overshoot suppression,tracking accuracy and response speed.
文摘The Equilibrium Optimizer(EO),Grey Wolf Optimizer(GWO),and Whale Optimizer(WO)algorithms are being recently developed for engineering optimization problems.In this paper,the EO,GWO,and WO algorithms are applied individually for a brushless direct current(BLDC)design optimization problem.The EO algorithm is inspired by the models utilized to find the system’s dynamic state and equilibrium state.The GWO and WO algorithms are inspired by the hunting behavior of the wolf and the whale,respectively.The primary purpose of any optimization technique is to find the optimal configuration by maximizing motor efficiency and/or minimizing the total mass.Therefore,two objective functions are being used to achieve these objectives.The first refers to a design with high power output and efficiency.The second is a constraint imposed by the reality that the motor is built into the wheel of the vehicle and,therefore,a lightweight is needed.The EO,GWO,and WOA algorithms are then utilized to optimize the BLDC motor’s design variables to minimize the motor’s total mass or maximize the motor efficiency by simultaneously satisfying the six inequality constraints.The simulation is carried out using MATLAB simulation software,and the simulation results prove the dominance of the proposed algorithms.This paper also suggests an efficient method from the proposed three methods for the BLDC motor design optimization problem.
基金supported by the National Science Foundation of China under Grant No.62066005Project of the Guangxi Science and Technology under Grant No.AD21196006.
文摘With the rapid development of communication technology,the problem of antenna array optimization plays a crucial role.Among many types of antennas,line antenna arrays(LAA)are the most commonly applied,but the side lobe level(SLL)reduction is still a challenging problem.In the radiation process of the linear antenna array,the high side lobe level will interfere with the intensity of the antenna target radiation direction.Many conventional methods are ineffective in obtaining the maximumside lobe level in synthesis,and this paper proposed a quantum equilibrium optimizer(QEO)algorithm for line antenna arrays.Firstly,the linear antenna array model consists of an array element arrangement.Array factor(AF)can be expressed as the combination of array excitation amplitude and position in array space.Then,inspired by the powerful computing power of quantum computing,an improved quantum equilibrium optimizer combining quantum coding and quantum rotation gate strategy is proposed.Finally,the proposed quantum equilibrium optimizer is used to optimize the excitation amplitude of the array elements in the linear antenna array model by numerical simulation to minimize the interference of the side lobe level to the main lobe radiation.Six differentmetaheuristic algorithms are used to optimize the excitation amplitude in three different arrays of line antenna arrays,the experimental results indicated that the quantum equilibrium optimizer is more advantageous in obtaining the maximum side lobe level reduction.Compared with other metaheuristic optimization algorithms,the quantum equilibrium optimizer has advantages in terms of convergence speed and accuracy.
基金This work was supported by the National Natural Science Foundation of China(62073093)the Postdoctoral Scientific Research Developmental Fund of Heilongjiang Province(LBH-Q19098)+1 种基金the Heilongjiang Provincial Natural Science Foundation of China(LH2020F017)the Key Laboratory of Advanced Marine Communication and Information Technology,Ministry of Industry and Information Technology.
文摘For bistatic multiple-input multiple-output(MIMO)radar,this paper presents a robust and direction finding method in strong impulse noise environment.By means of a new lower order covariance,the method is effective in suppressing impulse noise and achieving superior direction finding performance using the maximum likelihood(ML)estimation method.A quantum equilibrium optimizer algorithm(QEOA)is devised to resolve the corresponding objective function for efficient and accurate direc-tion finding.The results of simulation reveal the capability of the presented method in success rate and root mean square error over existing direction-finding methods in different application situations,e.g.,locating coherent signal sources with very few snapshots in strong impulse noise.Other than that,the Cramér-Rao bound(CRB)under impulse noise environment has been drawn to test the capability of the presented method.
基金supported by UGC SRF Fellowship(NTA Ref.Nos.211610092425 and 201610165233).
文摘Supply chains and other complex systems can be effectively managed and optimised with the help of optimal control techniques.Optimal control,as used in supply chain management,is the process of using mathematical optimisation techniques to identify the best course of action for controlling a given objective function over time.Modeling the supply chain’s dynamics,which include elements like production rates,inventory levels,demand trends,and transportation constraints,is the best control strategy when applied to a supply chain.In this study,we have considered that production rate is an unknown function of time,which is a controlling function.The demand for the product is taken as a function of price and time.The emission of carbon is taken as a linear function of the production rate of the system.To solve the suggested supply chain system,we have used an optimal control approach for determining the unknown production rate.To find the optimal values of the objective function as well as the decision variables,we have used different meta-heuristic algorithms and compared their results.It is observed that the equilibrium optimizer algorithm performed better than other algorithms used.Finally,a sensitivity analysis is performed,which is presented graphically in order to choose the best course of action.
文摘The paper discusses how to reach the equilibrium and optimization GI during the period of economic transformation. The market economy might not work because of its mechanism flaws, based on the assumption that the government is the supplier and the market economy is the demander Of GI, there is an equilibrium and optimization issue. The theory suggests that GI could reach equilibrium through adjusting the government revenue, thus leads to the result of functional complement between the market economy and the GI, and the optimum economic efficiency.
文摘This paper deals with the problem of nonconstant harvesting of prey in a ratio-dependent predator-prey system incorporating a constant prey refuge. Here we use the reasonable catch-rate function instead of usual catch-per-unit-effort hypothesis. The existence, as well as the stability of possible equilibria, is carried out. Bionomic equilibrium of the system is determined and optimal harvest policy is studied with the help of Pontryagin＇s maximum principle. The key results developed in this paper are illustrated using numer- ical simulations. Our results indicate that dynamic behavior of the system very much depends on the prey refuge parameter and increasing amount of refuge could increase prey density and may lead to the extinction of predator population density.
