Multi-objective Hierarchical Optimization of Interior Permanent Magnet Synchronous Machines Based on Rotor Surface Modification

To solve the problem of large torque ripple of interior permanent magnet synchronous motor(IPMSM),the rotor surface notch design method was used for V-type IPMSM.In order to accurately obtain the optimal parameter values to improve the torque performance of the motor,this paper takes the output torque capacity and torque ripple as the optimization objectives,and proposes a multi-objective layered optimization method based on the parameter hierarchical design combined with Taguchi method and response surface method(RSM).The conclusion can be drawn by comparing the electromagnetic performance of the motor before and after optimization,the proposed IPMSM based on the rotor surface notch design can not only improve the output torque,but also play an obvious inhibition effect on the torque ripple.

Hierarchical Optimization Method for Federated Learning with Feature Alignment and Decision Fusion

In the realm of data privacy protection,federated learning aims to collaboratively train a global model.However,heterogeneous data between clients presents challenges,often resulting in slow convergence and inadequate accuracy of the global model.Utilizing shared feature representations alongside customized classifiers for individual clients emerges as a promising personalized solution.Nonetheless,previous research has frequently neglected the integration of global knowledge into local representation learning and the synergy between global and local classifiers,thereby limiting model performance.To tackle these issues,this study proposes a hierarchical optimization method for federated learning with feature alignment and the fusion of classification decisions(FedFCD).FedFCD regularizes the relationship between global and local feature representations to achieve alignment and incorporates decision information from the global classifier,facilitating the late fusion of decision outputs from both global and local classifiers.Additionally,FedFCD employs a hierarchical optimization strategy to flexibly optimize model parameters.Through experiments on the Fashion-MNIST,CIFAR-10 and CIFAR-100 datasets,we demonstrate the effectiveness and superiority of FedFCD.For instance,on the CIFAR-100 dataset,FedFCD exhibited a significant improvement in average test accuracy by 6.83%compared to four outstanding personalized federated learning approaches.Furthermore,extended experiments confirm the robustness of FedFCD across various hyperparameter values.

Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing

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.

Adaptive connected hierarchical optimization algorithm for minimum energy spacecraft attitude maneuver path planning

Space object observation requirements and the avoidance of specific attitudes produce pointing constraints that increase the complexity of the attitude maneuver path-planning problem.To deal with this issue,a feasible attitude trajectory generation method is proposed that utilizes a multiresolution technique and local attitude node adjustment to obtain sufficient time and quaternion nodes to satisfy the pointing constraints.These nodes are further used to calculate the continuous attitude trajectory based on quaternion polynomial interpolation and the inverse dynamics method.Then,the characteristic parameters of these nodes are extracted to transform the path-planning problem into a parameter optimization problem aimed at minimizing energy consumption.This problem is solved by an improved hierarchical optimization algorithm,in which an adaptive parameter-tuning mechanism is introduced to improve the performance of the original algorithm.A numerical simulation is performed,and the results confirm the feasibility and effectiveness of the proposed method.

Static and dynamic design based on hierarchical optimization for materials and structure of porous metals

A static and dynamic collaborative optimization method for materials and structure with uniform periodic microstructure is presented.The sensitivity formulae of hierarchical optimization,i.e.,material design,structure design and integrated design for porous metals,are given.On the base of the hierarchical optimization model,numerical experiments of an MBB beam and a cantilever one were carried out.Based on porous metals bearing multi-functionality,the differences and applicability of hierarchical optimization are discussed in the structure loading field.It is concluded that structure design is mainly oriented to structure efficiency,material design is mainly oriented to multi-functionality,and integrated design is oriented to structure efficiency and multi-functionality.This work provides some useful ideas for the selection of porous metals design method.

Multi-objective Model Predictive Control of Grid-connected Three-level Inverter Based on Hierarchical Optimization

In order to solve the problem of weighting factors selection in the conventional finite-control-set model predictive control for a grid-connected three-level inverter,an improved multi-objective model predictive control without weighting factors based on hierarchical optimization is proposed.Four control objectives are considered in this strategy.The grid current and neutral-point voltage of the DC-link are taken as the objectives in the first optimization hierarchy,and by using fuzzy satisfaction decision,several feasible candidates of voltage vectors are determined.Then,the average switching frequency and common-mode voltage are optimized in the second hierarchy.The average ranking criterion is introduced to sort the objective functions,and the best voltage vector is obtained to realize the coordinated control of multiple objectives.At last,the effectiveness of the proposed strategy is verified by simulation results.

Product Family Hierarchical Associated Design and Its Hierarchical Optimization

After reviewing the literature and methodology-related issues within the field of product family design, a deficiency in the current design and development of product family is pointed out. The concept of hierarchical associated design is proposed in this article, according to the deficiency~ and the methods and models for realizing the notion above are described. A two-dimensional analytical model is constructed based on the composing levels and developing processes of product family. The optimization models to hierarchical associated design problems are grouped into two categories： overall-local and key-subordinate. The algorithms of the models are discussed in this paper

Multi-subject and multi-objective integrated optimization system and implementation strategy for energy-saving renovation of the existing residential buildings

The core of the healthy and orderly operation of the existing residential building energy-saving renovation market lies in the exploration of the implementation mechanism of multi-subject and multi-objective integrated optimization.The multi-agent and multi-objective integrated optimization system framework is a powerful tool to guide the scientific decision-making of the market core structural entities in the future market practice. This paper analyzes the practical dilemma of energy-saving renovation of theexisting residential buildings in China, summarizes the practical experience of multi-subject and multi-objective integrated optimization of energy-saving renovation of the existing residential buildings in foreign countries, and puts forward beneficial practical enlightenment on the basis of comparison at home and abroad;The design principles of the target integrated optimization system have established a multi-subject and multi-objective integrated optimization system framework for the energy-saving renovation of the existing residential buildings, from six aspects: government guidance, trust consensus, value co-creation, risk sharing, revenue sharing, and social responsibility sharing. This paper proposes a multi-subject and multi-objective integrated practice strategy, in order to promote the efficient and orderly development of China's existing residential building energy-saving renovation market.

A HIERARCHICAL IDENTIFICATION ALGORITHM FOR THE EXTENDED EXPONENTIAL MODEL

A new hierarchical identification algorithm is proposed for solving the identi-fication problem of the extended exponential model,which is used frequently in ecological,social and economical systems.By using the zero character of the optimal Lagrangianmultipliers of the equivalent identification problem,a two-level structure of the algorithmis derived first.Then,the convergence and the correspondence with the conventionalnonlinear approaches of the algorithm are proved.The results of simulation and applica-tion show that its convergent rate is greatly higher than that of the L-Mmethod.

Research on the Development Model of University Archives Cultural Products Based on Deep Learning

The products of an archival culture in colleges and universities are the final result of the development of archival cultural resources,and the development of archival cultural effects in colleges and universities should be an important part of improving the artistic level of libraries.The existing RippleNet model doesn’t consider the influence of key nodes on recommendation results,and the recommendation accuracy is not high.Therefore,based on the RippleNet model,this paper introduces the influence of complex network nodes into the model and puts forward the Cn RippleNet model.The performance of the model is verified by experiments,which provide a theoretical basis for the promotion and recommendation of its cultural products of universarchives,solve the problem that RippleNet doesn’t consider the influence of key nodes on recommendation results,and improve the recommendation accuracy.This paper also combs the development course of archival cultural products in detail.Finally,based on the Cn-RippleNet model,the cultural effect of university archives is recommended and popularized.

Wind Turbine Optimal Preventive Maintenance Scheduling Using Fibonacci Search and Genetic Algorithm

Maintenance scheduling is essential and crucial for wind turbines (WTs) to avoid breakdowns andreduce maintenance costs. Many maintenance models have been developed for WTs’ maintenance planning, suchas corrective, preventive, and predictive maintenance. Due to communities’ dependence on WTs for electricityneeds, preventive maintenance is the most widely used method for maintenance scheduling. The downside tousing this approach is that preventive maintenance (PM) is often done in fixed intervals, which is inefficient. In thispaper, a more detailed maintenance plan for a 2 MW WT has been developed. The paper’s focus is to minimize aWT’s maintenance cost based on a WT’s reliability model. This study uses a two-layer optimization framework:Fibonacci and genetic algorithm. The first layer in the optimization method (Fibonacci) finds the optimal numberof PM required for the system. In the second layer, the optimal times for preventative maintenance and optimalcomponents to maintain have been determined to minimize maintenance costs. The Monte Carlo simulationestimates WT component failure times using their lifetime distributions from the reliability model. The estimatedfailure times are then used to determine the overall corrective and PM costs during the system’s lifetime. Finally,an optimal PM schedule is proposed for a 2 MW WT using the presented method. The method used in this papercan be expanded to a wind farm or similar engineering systems.

Loyal wingman task execution for future aerial combat:A hierarchical prior-based reinforcement learning approach

In modern Beyond-Visual-Range(BVR)aerial combat,unmanned loyal wingmen are pivotal,yet their autonomous capabilities are limited.Our study introduces an advanced control algorithm based on hierarchical reinforcement learning to enhance these capabilities for critical missions like target search,positioning,and relay guidance.Structured on a dual-layer model,the algorithm’s lower layer manages basic aircraft maneuvers for optimal flight,while the upper layer processes battlefield dynamics,issuing precise navigational commands.This approach enables accurate navigation and effective reconnaissance for lead aircraft.Notably,our Hierarchical Prior-augmented Proximal Policy Optimization(HPE-PPO)algorithm employs a prior-based training,prior-free execution method,accelerating target positioning training and ensuring robust target reacquisition.This paper also improves missile relay guidance and promotes the effective guidance.By integrating this system with a human-piloted lead aircraft,this paper proposes a potent solution for cooperative aerial warfare.Rigorous experiments demonstrate enhanced survivability and efficiency of loyal wingmen,marking a significant contribution to Unmanned Aerial Vehicles(UAV)formation control research.This advancement is poised to drive substantial interest and progress in the related technological fields.

Sequential maneuvering decisions based on multi-stage influence diagram in air combat

A multi-stage influence diagram is used to model the pilot＇s sequential decision making in one on one air combat. The model based on the multi-stage influence diagram graphically describes the elements of decision process, and contains a point-mass model for the dynamics of an aircraft and takes into account the decision maker＇s preferences under uncertain conditions. Considering an active opponent, the opponent＇s maneuvers can be modeled stochastically. The solution of multistage influence diagram can be obtained by converting the multistage influence diagram into a two-level optimization problem. The simulation results show the model is effective.

Rescheduling Problems with Bicriteria

In the rescheduling on a single machine,a set of original jobs has already been scheduled to minimize some cost objective,when a new set of jobs arrives and creates a disruption.The decision maker needs to insert the new jobs into the existing schedule without excessively disrupting it.In this paper,we consider hierarchical optimization between the scheduling cost of all the jobs and the degree of this disruption.For every problem,we provide either a polynomial time algorithm or an intractable result.

Optimal Dispatching of Large-scale Water Supply System

This paper deals with the use of optimal control techniques in large-scale water distribution networks. According to the network characteristics and actual state of the water supply system in China, the implicit model, which may be solved by utilizing the hierarchical optimization method, is established. In special, based on the analyses of the water supply system containing variable-speed pumps, a software tool has been developed successfully. The application of this model to the city of Shenyang (China) is compared to experiential strategy. The results of this study show that the developed model is a very promising optimization method to control the large-scale water supply systems.

Optimal design of linear switched reluctance motor for sea wave power generation

The switchless reluctance motor’s non-permanent magnet structure design ensures its high reliability in the marine environment;thus,it is a feasible solution for the generator of a sea wave power generation system.However,the corresponding thrust density and efficiency remain insufficient.This study focused on a new type of flat linear switched reluctance motor(LSRM),using the finite element software to establish a structural model,and optimized the design with the goal of improving the efficiency and energy density.The entropy method was adopted for sensitivity stratification to objectively select weights to avoid the influence of subjectively selected different proportional weights on the optimization results.Based on the entropy method,the sensitivity of different structural parameters was stratified,and the simulated annealing algorithm,response surface method,and single parameter scanning method were combined for optimization.Finally,the optimal structural size parameters of the motor were determined.Based on the two-dimensional finite element method,to simulate the electromagnetic performance of the reluctance motor under different operating conditions,such as thrust,loss,and efficiency,changes in motor performance before and after optimization were compared to verify the high power generation efficiency and energy density of the optimized linear motor.

Convexification and Hierarchical Optimization Algorithm for Distribution of Water Supply Systems

This paper deals with the optimal distribution problem of large urban water supply systems in our country.The mathematical model of the problem is developed.It possesses the separable,but nonconvex structure.In order for primal-dual methods to be applicable to this type of largescale and nonconvex optimization problem,a method by means of which a nonconvex problem is convexified is studied based on the principle of the multiplier methods in the paper.The decomposition-coordination optimization algorithm is proposed based on the convexification method and the multiplier methods.The algorithm has been simulated in some real urban water supply systems,and the satisfactory results are obtained.

A Hierarchical Transactive Energy Management Framework for Optimizing the Reserve Profile in District Energy Systems

District energy systems(DESs)have become a popular form of satisfying comprehensive energy demands for different types of loads in multiple local buildings.For DFISs,the operational flexibility could be maintained by energy conversion and storage facilities.This paper proposes a hierarchical optimization framework for leveraging and aggregating the DES flexibility to provide contingency reserves.To characterize and quantify the flexibility in individual DESs,the concept of available reserve profile,which is measured by a set of indices,is established.A two-stage robust optimization(RO)model is developed for calculating the indices,which considers the uncertainties associated with wind power and ambient temperature.The lower stage of the two-stage model is managed by district energy system operators(DESOs)which submit reserve profiles to the district energy system coordinator(DESC)at the upper stage,which is responsible for the coordination process.Correspondingly,information privacy is preserved using a coordinated data-sharing strategy.Using reserve profiles submitted by multiple DESOs,the DESC applies the proposed coordination model to provide a certain reserve capacity schedule to DESs,which satisfies the stated objectives.The coordination model is formulated and solved based on the special ordered set(SOS)technique and particle swarm optimization(PSO)algorithm.A test system is developed to illustrate the technical viability and economic feasibility of the proposed technique.

Distributed receding horizon control for fuel-efficient and safe vehicle platooning

This paper investigates the problem of fuel-efficient and safe control of autonomous vehicle platoons. We present a two-part hierarchical control method that can guarantee platoon stability with minimal fuel consumption. The first part vehicle controller is derived in the context of receding horizon optimal control by constructing and solving an optimization problem of overall fuel consumption. The Second part platoon controller is a complementation of the first part, which is given on the basis of platoon stability analysis. The effectiveness of the presented platoon control method is demonstrated by both numerical simulations and experiments with laboratory-scale Arduino cars.