Enhanced Hybrid Equilibrium Strategy in Fog-Cloud Computing Networks with Optimal Task Scheduling

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.

Multi-Objective Equilibrium Optimizer for Feature Selection in High-Dimensional English Speech Emotion Recognition

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.

THE OPTIMAL DEDUCTIBLE AND COVERAGE IN INSURANCE CONTRACTS AND EQUILIBRIUM RISK SHARING POLICIES

In this paper, we consider the optimal risk sharing problem between two parties in the insurance business: the insurer and the insured. The risk is allocated between the insurer and the insured by setting a deductible and coverage in the insurance contract. We obtain the optimal deductible and coverage by considering the expected product of the two parties' utilities of terminal wealth according to stochastic optimal control theory. An equilibrium policy is also derived for when there are both a deductible and coverage;this is done by modelling the problem as a stochastic game in a continuous-time framework. A numerical example is provided to illustrate the results of the paper.

Quantum-Inspired Equilibrium Optimizer for Linear Antenna Array

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.

An Enhanced Equilibrium Optimizer for Solving Optimization Tasks

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.

Magnetic diagnostics layout design for CFETR plasma equilibrium reconstruction

Plasma equilibrium reconstruction provides essential information for tokamak operation and physical analysis.An extensive and reliable set of magnetic diagnostics is required to obtain accurate plasma equilibrium.This study designs and optimizes the magnetic diagnostics layout for the reconstruction of the equilibrium of the plasma according to the scientific objectives,engineering design parameters,and limitations of the Chinese Fusion Engineering Test Reactor(CFETR).Based on the CFETR discharge simulation,magnetic measurement data are employed to reconstruct consistent plasma equilibrium parameters,and magnetic diagnostics'number and position are optimized by truncated Singular value decomposition,verifying the redundancy reliability of the magnetic diagnostics layout design.This provides a design solution for the layout of the magnetic diagnostics system required to control the plasma equilibrium of CFETR,and the developed design and optimization method can provide effective support to design magnetic diagnostics systems for future magnetic confinement fusion devices.

Optimal and Memristor-Based Control of A Nonlinear Fractional Tumor-Immune Model

In this article,the reduced differential transform method is introduced to solve the nonlinear fractional model of Tumor-Immune.The fractional derivatives are described in the Caputo sense.The solutions derived using this method are easy and very accurate.The model is given by its signal flow diagram.Moreover,a simulation of the system by the Simulink of MATLAB is given.The disease-free equilibrium and stability of the equilibrium point are calculated.Formulation of a fractional optimal control for the cancer model is calculated.In addition,to control the system,we propose a novel modification of its model.This modification is based on converting the model to a memristive one,which is a first time in the literature that such idea is used to control this type of diseases.Also,we study the system’s stability via the Lyapunov exponents and Poincare maps before and after control.Fractional order differential equations(FDEs)are commonly utilized to model systems that have memory,and exist in several physical phenomena,models in thermoelasticity field,and biological paradigms.FDEs have been utilized to model the realistic biphasic decline manner of elastic systems and infection of diseases with a slower rate of change.FDEs are more useful than integer-order in modeling sophisticated models that contain physical phenomena.

Application of the Conditional Nonlinear Optimal Perturbations Method in a Theoretical Grassland Ecosystem

用曾等建议的一个简化非线性地理论的草地生态系统，我们与线性地稳定的草地(荒芜或潜伏的沙漠)说的 .The 结果表演能转与有限振幅非线性地不稳定的有条件的非线性的最佳的不安( CNOP )的途径学习敏感和草地生态系统的非线性的不稳定性到有限振幅的起始的不安 降水在之间的起始的 perturbations.When 二分叉指，大

The Optimal Solution of the Model with Physical and Human Capital Adjustment Costs

We prove that the model with physical and human capital adjustment costs has optimal solution when the production function is increasing return and the structure of vetor fields of the model changes substantially when the prodution function from decreasing return turns to increasing return. And it is shown that the economy is improved when the coefficients of adjustment costs become small. Key words optimal solution - nonzero equilibrium - adjustment costs CLC number O 29 Foundation item: Supported by the National Natural Science Foundation of China (79970104)Biography: RAO Lan-lan (1978-), female, Master candidate, research direction: mathematical economy.

Distributive Disturbance and Optimal Policy in Stochastic Control Model

To investigate the equilibrium relationships between the volatility of capital and income, taxation, and ance in a stochastic control model, the uniqueness of the solution to this model was proved by using the method of dynamic programming under the introduction of distributive disturbance and elastic labor supply. Furthermore, the effects of two types of shocks on labor-leisure choice, economic growth rate and welfare were numerically analyzed, and then the optimal tax policy was derived.

Optimal Tariff and Endogenous Drivers for Trade Liberalization

Trade liberalization requires institutional coordination and openness,and is driven by a multitude of factors.This paper simulates endogenous optimal tariffs under different model structures to uncover the underlying drivers of trade liberalization.Parametric calibration and simulation methods based on the numeric general equilibrium model are employed to estimate the optimal tariff rates of countries with and without trade retaliation.Then,near-reality assumptions are added into the standard general equilibrium model structure,including the cross-border capital flow,multi-country assumption and trade cost,to simulate decreasing optimal tariff rates.The simulation results suggest that world economic development has increased the economic ties and interdependence among nations,making trade liberalization an endogenous optimal choice.The backlash against globalization in recent years is motivated by short-term factors,but will not persist in the long run since it goes against the law of economic growth and socio-economic development.

Time Optimal Control in Time Series Movement

The paper analyses time series that exhibit equilibrium states. It analyses the formation of equilibrium and how the system can return to the aforementioned equilibrium. The tool that is used in the aforementioned analysis is time optimal control in the phase plane. It is proved that equilibrium state is sustainable if initial state is not too far from the equilibrium as well as control vector is large enough. On the other hand, if initial state is one standard deviation away from equilibrium state, it is proved that equilibrium cannot be reached. It is the same case with control vector. If it is unbounded, time optimal control cannot be applied. The approach that is introduced represents unconventional method of analysing equilibrium in time series.

Optimizing Polynomial-Time Solutions to a Network Weighted Vertex Cover Game

Weighted vertex cover(WVC)is one of the most important combinatorial optimization problems.In this paper,we provide a new game optimization to achieve efficiency and time of solutions for the WVC problem of weighted networks.We first model the WVC problem as a general game on weighted networks.Under the framework of a game,we newly define several cover states to describe the WVC problem.Moreover,we reveal the relationship among these cover states of the weighted network and the strict Nash equilibriums(SNEs)of the game.Then,we propose a game-based asynchronous algorithm(GAA),which can theoretically guarantee that all cover states of vertices converging in an SNE with polynomial time.Subsequently,we improve the GAA by adding 2-hop and 3-hop adjustment mechanisms,termed the improved game-based asynchronous algorithm(IGAA),in which we prove that it can obtain a better solution to the WVC problem than using a the GAA.Finally,numerical simulations demonstrate that the proposed IGAA can obtain a better approximate solution in promising computation time compared with the existing representative algorithms.

SCR脱硝系统的RBF条件积分滑模参数优化控制

针对选择性催化还原(SCR)脱硝系统在火电机组深度调峰下因系统特性随着机组负荷改变而难以控制的问题,提出基于径向基函数(RBF)的条件积分滑模优化控制方案,用条件积分滑模减少抖振与调节时间,用RBF实时逼近SCR脱硝系统特性改变下的未知扰动以提高系统鲁棒性。并提出一种纳什均衡量子粒子群寻优(NEQPSO)算法,以获得RBF条件积分滑模控制方案的最优参数,并在20%~100%的机组负荷下进行仿真实验。结果表明:优化后的RBF条件积分滑模控制与传统控制方案相比超调量降低50.26%、调节时间缩短13.55%,在干扰信号下超调量仅有1.50%,其响应速度更快、抗干扰能力更强、鲁棒性更好。

一种基于伪谱法的助推-补能跳跃滑翔轨迹优化方法

针对大气层边缘补能-跳跃滑翔运动,建立了一种助推-补能跳跃滑翔全程空间运动模型以及补能滑翔段无量纲运动方程。提出了一种基于Gauss伪谱法的动态分段串行优化策略,可对助推段俯仰程序角、补能-跳跃滑翔段飞行攻角、倾侧角以及补能时刻等轨迹控制变量进行求解,实现含连续/离散控制变量的轨迹优化一体化设计。分别对助推-补能跳跃滑翔和助推-拟平衡滑翔轨迹优化开展仿真,验证了针对补能-跳跃滑翔轨迹的分段串行优化策略的有效性,同时分析了相同助推规模和飞行器质量条件下,补能跳跃轨迹相较拟平衡滑翔轨迹在增大飞行时间、航程和机动过载能力上更具优势。

多品种电力市场交易下负荷聚合商投标策略及市场均衡分析

中国新型电力系统建设正面临灵活资源短缺等问题,亟须发挥负荷侧调节能力。随着电网与负荷双向交互越发频繁,更多负荷聚合商通过整合辖区灵活资源参与市场交易,导致以往由发电侧占主导的市场均衡被打破。为研究多主体负荷聚合商博弈策略,构建了能量-备用电力市场下负荷聚合商双层投标模型,并引入多场景描述非决策者投标不确定性对市场均衡的影响。通过强平稳算法(SSM)将多个双层模型转化为带均衡约束的均衡问题(EPEC)模型,并采用大M法、二进制展开法等线性化方法进行线性化。算例结果验证了模型及求解算法的合理性和有效性,表明基于EPEC模型的负荷聚合商最优投标策略更加契合实际市场规则,有助于系统优化负荷曲线,缓解灵活资源供需紧张。

基于可变生成概率和多差分柯西变异的均衡优化算法

针对标准均衡优化算法(EO)存在全局搜索和局部搜索的平衡能力不足以及易陷入局部最优的问题,提出了一种基于可变生成概率和多差分柯西变异的均衡优化算法(Variable generation probability and multi-difference Cauchy variation equilib-rium optimization algorithm,VDEO)。首先,结合Tent混沌映射增加初始化种群的多样性,为寻优提供基础;其次,引入可变的生成概率代替原始的固定值,使算法在迭代前期增加全局搜索能力,后期关注求解精度,以提升全局搜索和局部搜索的平衡能力;最后,融合多种差分策略和柯西变异帮助寻优过程跳出局部最优。针对包含单峰、多峰和固定维多峰在内的15个基准测试函数和CEC2022测试函数,将VDEO在多种维数下与EO,GWO,WOA,SCA,MFO,AOA,AVOA,BWO,AHA,POA这10个启发式算法进行仿真对比实验,并对基准测试函数的实验结果进行Wilcoxon秩和检验,实验结果表明,VDEO实现了更好的全局搜索和局部搜索的平衡,并具有更好的跳出局部最优的能力以及更高的收敛精度。

山区铁路桥隧工程关键节材要素均衡优化

为在经济可承受范围内提升山区铁路的节材性能,提出了一种基于NSGA-Ⅱ的关键节材要素双目标均衡优化方法。首先,以断面超挖、喷射混凝土类型选择、围岩变形预留、桩基扩孔、桩头超封5个桥隧工程关键节材要素为决策变量,以CO_(2)减排量最大、建造成本增加值最小为优化目标,构建山区铁路桥隧工程关键节材要素均衡优化模型;其次,采用带精英策略的非支配排序遗传算法(NSGA-Ⅱ)求出Pareto均衡解集;再次,耦合主客观元素组合的综合赋权法与TOPSIS法对Pareto均衡解集进行科学决策,得到考虑决策者偏好的关键节材要素最佳组合,并分析决策者偏好对最佳组合的影响;最后,以某山区铁路工程某标段为例,系统地验证了该方法的科学性及适用性。研究结果表明:与优化前相比,最佳组合能实现CO_(2)减排12.622万t,对应建造成本增加了24.833万元,优化效率达到50.826%。

不确定环境下收费与信息对路径选择行为影响的实验研究

在不确定环境下考虑信息发布与拥挤收费两种措施,基于交通网络均衡,推导了两路径简单路网的最优收费模型,并推广至一般路网。基于两路径路网设计逐日路径选择行为实验,分析了两种措施对路径选择行为的影响。结果表明:完美信息与收费的实施均能减少流量波动,单独实施收费的效果最佳,但只有两种措施相结合时,路网流量趋于用户均衡的趋势最稳定;完美信息会增加路径切换行为,而收费可有效抑制大幅的路径切换;无收费时路径切换往往会造成出行成本增加,而收费时则相反;收费与信息结合时的出行时间比两种措施单独实施时小。

开放经济动态随机一般均衡模型下最优货币政策和货币状况指数研究——基于1992—2022年中国数据的理论和实证分析

本文构建开放经济下的新凯恩斯动态随机一般均衡模型,模拟显示在外部冲击下,利率、汇率和最终目标高度相关。以理论模型为基础对最优货币政策进行探究,发现其政策权重受消费者、生产者和中央银行的行为等模型参数的影响。进一步构建货币状况指数(MCI),其权重不仅取决于商品市场、货币市场和利率平价参数等的变化,还依赖于最优货币政策参数的变化。根据1992—2022年数据进行实证研究发现,最优货币政策参数为1.1823,意味着我国央行的货币政策是逆周期操作的,且赋予经济增长以更大的权重;估计出MCI的权重为0.7867,意味着实际利率上升1%相当于实际汇率下降0.79%,实际利率变动的影响弱于实际汇率。最后本文研究表明货币状况指数可以作为宏观经济目标变动的先行指标。