Hierarchical Optimization Method Research Articles

Design and fabrication of polygonal grating waveguide display with full-color 2D eye-box expansion

Over the past few decades, the surface relief grating (SRG) based waveguide display technology has attracted significant interest in the augmented reality community due to its light weight, small form factor and ease of mass production. However, the intuition grating structure restricts the optimization freedom which limits the display quality of full-color SRG waveguide. To address this issue the 2D polygonal grating is presented since it has a higher freedom degree in optimizing color uniformity compared to intuition grating structure. By using the 2D polygonal grating we also proposed a design approach of full-color SRG waveguide with hierarchical optimization method. To validate this design, a 2D polygonal grating-based waveguide composed of double layers waveguide was fabricated by using EBL and nanoimprinting technology. The final experimental results show that the 2D eye-box expansion (EPE) waveguide has a field of view (FOV) of 40°, an eye-box of 15 mm × 12 mm, at an eye relief (ERF) of 25 mm, and the overall luminance uniformity is 60 %.

Data-driven hierarchical collaborative optimization method with multi-fidelity modeling for aerodynamic optimization

The aerodynamic optimization of aircraft based on evolutionary algorithms generally requires thousands or even tens of thousands of iterations. Moreover, it faces challenges such as high-dimensional design variables and high computational costs, leading to low efficiency in algorithm search and poor optimization results. In order to enhance the efficiency of aerodynamic optimization, a data-driven collaborative optimization and multi-fidelity hierarchical modeling method is proposed. Firstly, a Clustering-Enhanced Teaching-Learning-Based Optimization (CETLBO) algorithm is introduced to improve its exploration and exploitation capabilities. By employing a collaborative optimization strategy, a divide-and-conquer paradigm is utilized to efficiently solve high-dimensional, nonlinear, and complex engineering optimization problems. Secondly, a two-stage hierarchical aerodynamic optimization framework is designed to reduce computational costs by integrating data from different fidelity levels. Stage 1 adopts a data-driven approximate optimization method to provide prior knowledge for the high-fidelity optimization of stage 2. Finally, the proposed method is applied to the optimization design of supersonic wing shapes. Compared to traditional optimization design systems, it demonstrates high efficiency in high-dimensional aerodynamic optimization problems, yielding better optimization results.

Non-Weighted Two-Stage Model Predictive Control Strategy Based on Three-Level NPC Inverter

This paper investigates the asynchronous motors driven by a Three-Level Neutral-Point-Clamped Voltage Source Inverter (3L-NPC-VSI) and aims to achieve control without weight factors and reduce torque ripple. It puts forward a non-weighted two-stage Finite-Control-Set Model Predictive Control (FCS-MPC) strategy. First, a hierarchical optimization method is adopted to address the difficulty of setting weight factors in traditional FCS-MPC applications. The method offers stratified designs of three performance indices, voltage jump, common-mode voltage, and current tracking, obviating the need for weight factor setting and reducing the calculation load of predictions. Secondly, to further mitigate torque ripple, an optimal vector or vector combination is implemented at the current control layer by adhering to the principle of minimal current tracking error. During the selection of the optimal vector combination, the first vector of the combination is chosen to be the vector at the end of the present cycle. This ensures that there is at most one switch within each control period, reducing the switching losses of the two-stage FCS-MPC. Lastly, detailed simulation and experimental analyses are conducted to verify the feasibility and effectiveness of the proposed strategy.

Multi-objective Hierarchical Optimization Method with Decoupling Capability for Hybrid-Magnetic-Circuit Variable Flux Memory Machine

Multi-objective Hierarchical Optimization Method with Decoupling Capability for Hybrid-Magnetic-Circuit Variable Flux Memory Machine

Cross-subject emotion recognition using hierarchical feature optimization and support vector machine with multi-kernel collaboration

Objective. Due to individual differences, it is greatly challenging to realize the multiple types of emotion identification across subjects. Approach. In this research, a hierarchical feature optimization method is proposed in order to represent emotional states effectively based on peripheral physiological signals. Firstly, sparse learning combined with binary search is employed to achieve feature selection of single signals. Then an improved fast correlation-based filter is proposed to implement fusion optimization of multi-channel signal features. Aiming at overcoming the limitations of the support vector machine (SVM), which uses a single kernel function to make decisions, the multi-kernel function collaboration strategy is proposed to improve the classification performance of SVM. Main results. The effectiveness of the proposed method is verified on the DEAP dataset. Experimental results show that the proposed method presents a competitive performance for four cross-subject types of emotion identification with an accuracy of 84% (group 1) and 85.07% (group 2). Significance. The proposed model with hierarchical feature optimization and SVM with multi-kernel function collaboration demonstrates superior emotion recognition accuracy compared to state-of-the-art techniques. In addition, the analysis based on DEAP dataset composition characteristics presents a novel perspective to explore the emotion recognition issue more objectively and comprehensively.

Hierarchical Operation Optimization for Regenerative Braking Energy Utilizing in Urban Rail Traction Power Supply System

The energy feedback system (EFS) is widely accepted to utilize the regenerative braking energy (RBE) in an urban rail traction power supply system (TPSS). However, the sharing relationship of RBE between EFS, traction trains and on-board braking resistors is not clear. In addition, the impact of EFS operation on the sharing of RBE has been rarely studied. This paper proposed a hierarchical operation optimization method for improving the utilization of shared RBE in TPSS through the EFS. An optimizing model for the dynamic start-up voltage threshold of EFS is established, with the objective of minimizing TPSS power consumption. A fast prediction model of train operation information is developed to analyze the steady-state power flow in advance. The optimal solution is searched using a salp swarm algorithm (SSA) on a per second basis. A microsystem of three traction stations and two trains is analyzed. Compared to the conventional constant voltage operation scheme, the optimal solution achieves a maximum additional energy-saving efficiency improvement of 2.44%. Efficient sharing of RBE is identified as the key to achieving energy savings. Regarding the local control part, system stability analysis is verified. Real-time simulation results indicate that the dynamic operating mode of EFS efficiently distributes RBE.

Hierarchical Optimization Based on Deep Reinforcement Learning for Connected Fuel Cell Hybrid Vehicles through Signalized Intersections

With the advantages of non-pollution and energy-saving, hydrogen fuel cell hybrid vehicles (HFCHVs) are regarded as one of the potential traveling ways in the future. The energy management of FCHVs has a huge energy-efficient potential which is combined with the Internet of Things (IOT) and auto-driving technologies. In this paper, a hierarchical joint optimization method that combines deep deterministic policy gradient and dynamic planning (DDPG-DP) for speed planning and energy management of the HFCHV is proposed for urban road driving scenarios. The results demonstrate that when the HFCHV is operating in driving scenario 1, the traveling efficiency of the DDPG-DP algorithm is 17.8% higher than that of the IDM-DP algorithm, and the hydrogen fuel consumption is reduced by 2.7%. In contrast, the difference in the traveling efficiency and fuel economy is small among the three algorithms in driving scenario 2, the number of idling/stop situations of the DDPG-DP algorithm is reduced compared with that of the IDM-DP algorithm. This will support further research for multi-objective eco-driving optimization of fuel cell hybrid vehicles.

Hierarchical shape optimization for large deployable membrane reflector with spatial skirt cable

In the shape optimization analysis of electrostatically controlled deployable membrane reflector (ECDMR), the poor balance of the membrane with spatial skirt cable is the main reason for the degradation of the reflector surface precision. At the same time, while in the optimization of the pre-stress of large deployable reflectors, the membrane structure has strong geometric nonlinearity, and the lack of necessary gradient information leads to low efficiency of the calculation. Therefore, according to the structural characteristics of the cable-membrane reflector, the two optimization objectives of the highest surface precision and the most uniform stress distribution is considered, and a hierarchical shape optimization method is proposed based on membrane theory and unbalance force analysis, which realizes the solution of the multi-objective optimization problem when the three optimization objectives have different priorities. The interior effective membrane reflective surface is analyzed using membrane theory, and the external membrane with spatial skirt cable is analyzed using nonlinear finite element method. The gradient information required for optimization is also obtained by unbalance force analysis, which ensures better optimization efficiency and can solve large-scale optimization problems. Therefore, the node positions and element equilibrium stresses of the structure can be optimized simultaneously, which further expands the search space of the equilibrium state and reduces the possibility of falling into local optimal solutions. Finally, a numerical example of a 30 m aperture ECDMR is calculated and analyzed, and the results show that the proposed method can obtain a high surface precision with a fast solution speed.

Hierarchical eco-driving control for plug-in hybrid electric vehicles under multiple signalized intersection scenarios

With the development of vehicle-to-everything (V2X) and autonomous driving technologies, plug-in hybrid electric vehicles (PHEVs) enable to absorb surrounding information to enhance economic driving. This study proposes a dynamic inverse hierarchical optimization method, which incorporates traffic-signal phase and timing as well as road data, to plan economic velocity and improve PHEV energy consumption. The hierarchical control framework determines the desired speed and arrival time in the upper layer using the shortest path faster algorithm. The lower level accounts for multi-objective velocity planning based on particle swarm optimization and Pareto theory. The inverse layering method solves the economic velocity optimization problem. With the support of V2X and autonomous driving technologies, the method enhances energy efficiency and computational efficiency in PHEVs through dynamic inverse hierarchical optimization. Simulation results highlight that the proposed algorithm leads to 7.43% improvement in energy consumption economy and the reduced calculation time, compared to the existing solutions. The hardware-in-the-loop experiments validate the real-time applicability of the proposed algorithm.

Federated learning application in distributed energy trading in integrated energy system

Privacy protection in electricity market transactions is a long-term topic, and it must be considered in the application of any new power system project today. This paper explores a new welfare optimization method considering privacy protection in the field of energy trading based on federated learning (FL). First, this paper models an integrated energy system (IES) with one distributed network manager (DNM) and multiple load aggregators (LAs). This model adopts the idea of Stackelberg game, makes DNM dominate the trading game, and makes LAs as followers. Then, it uses the FL-Stackelberg method proposed for the first time to solve the established individual welfare optimization model participating in the game and compares the results with those of the traditional hierarchical optimization method and the Mathematical Program with Equilibrium Constraint optimization method. Numerical results at the end of the paper prove the good accuracy and calculation speed of the proposed FL-Stackelberg​ optimization algorithm, and demonstrate the reliability of FL in engineering practice of IES.

An effective approach for deriving and evaluating approximate optimal design solutions of energy supply systems by time series aggregation

It is important to design multi-energy supply systems optimally in consideration of their operations for variations in energy demands. An approach for efficiently solving such an optimal design problem with a large number of periods for variations in energy demands is to derive an approximate optimal design solution by time series aggregation. However, such an approach does not provide any information on the accuracy for the optimal value of the objective function. In this paper, an effective approach for time series aggregation is proposed to derive an approximate optimal design solution and evaluate a proper gap between the upper and lower bounds for the optimal value of the objective function based on a mixed-integer linear model. In accordance with aggregation, energy demands are relaxed to uncertain parameters and the problem for deriving an approximate optimal design solution and evaluating it is transformed to a three-level optimization problem, and it is solved by applying both the robust and hierarchical optimization methods. A case study is conducted on a cogeneration system with a practical configuration, and it turns out that the proposed approach enables one to derive much smaller gaps as compared with those obtained by a conventional approach.

Distributed Consensus Hierarchical Optimization and Control Method for Integrated Energy System Based on Event-Triggered Mechanism

For integrated energy systems (IES) composed of a set of energy hubs (EHs), a consensus control method is usually adopted to achieve accurate sharing of electrical and thermal composite energies. To solve the communication redundancy problem of the consensus control method, a hierarchical optimization and distributed control scheme based on a dynamic event-triggered mechanism of EHs is proposed to realize stable operation of IES. An economic optimization strategy based on equal increment principle is improved to minimize the operation costs of IES in the second layer. Due to consensus control being integrated into the supply-demand power deviation calculations of EHs, the desired electrical and thermal power trajectories are accurately determined. To improve dynamic response performances in the presence of uncertain disturbances, an event-triggered communication mechanism is designed in the primary layer. The triggering threshold can be adjusted dynamically according to changes of electrical and thermal power outputs, and the redundant communication requirement in the electrical branches is reduced. Considering the coupling characteristics of IES energy networks, a consensus control method is promoted to synchronously track the desired electric and thermal power trajectories of EHs, and the goal of accurate power sharing is achieved. The frequency and pipeline pressure fluctuations are also limited within the allowable range. The economic optimization and coordinated operation of electrical and thermal composite energies in IES are guaranteed by the proposed hierarchical control structure. Additionally, only information from neighboring EHs at the event-triggered time is involved, so the computation simplicity and control performance can be obtained simultaneously. The hardware-in-loop experimental results are conducted to demonstrate the effectiveness of the proposed control strategy.

Cooperative Power Bidding of Smart Community Grids With an Aggregator-Prosumer-Based Hierarchical Framework

The progressive development of electricity markets envisions the participation of smart community grids in energy trading, but it is still challenging to guarantee global optimality for uncertain power management of multiple entities under a hierarchical framework. To tackle the cooperative strategic bid-ding problem with uncertainties, this paper proposes a hierar-chical three-stage robust optimization (HTS-RO) method. With respect to the bilayer coordination framework involving an ag-gregator gathering multiple prosumers, a novel HTS-RO bidding model is firstly established relying on the canonical centralized two-stage RO model. In the first stage, the aggregator performs power bidding in the upper layer according to the derived power flow information of the shared buses from the lower-layer prosumers. Considering renewable-load uncertainties and pri-vacy preservation, each prosumer conducts its own robust scheduling via the second and third stages based on the bidding prices from the aggregator and submits its optimal results of the shared bus back to the aggregator. For the robust scheduling model of each prosumer, the second stage identifies the basic plans of the nominal scenario, and the third stage checks the fea-sibility of the basic plans under uncertainties. To solve the re-sulting intractable multi-level mixed-integer nonlinear pro-gramming, a tri-loop decomposition-based iterative algorithm is tailored to ensure that the distributed computation converges to the optimal solution with the overall profit, thereby achieving the equivalent split of the centralized optimization into a hierarchical manner. Numerical tests on generic smart community grids with multiple prosumers validate the applicability and superiority of the proposed HTS-RO method for power bidding.

Resource Allocation for a Secure SWIPT Network Based on a Quantitative Energy Harvesting Mechanism.

Simultaneous wireless information and power transfer (SWIPT) technology can effectively extend the lifecycle of energy-constrained networks. In order to improve the energy harvesting (EH) efficiency and network performance in secure SWIPT networks, this paper studies the resource allocation problem based on the quantitative EH mechanism in the secure SWIPT network. Based on a quantitative EH mechanism and nonlinear EH model, a quantified power-splitting (QPS) receiver architecture is designed. This architecture is applied in the multiuser multi-input single-output secure SWIPT network. With the goal of maximizing the network throughput, the optimization problem model is established under the conditions of meeting the legal user's signal-to-interference-plus-noise ratio (SINR), EH requirements, the total transmit power of the base station, and the security SINR threshold constraints. Due to the coupling of variables, the problem is a nonconvex optimization problem. To deal with the nonconvex optimization problem, a hierarchical optimization method is adopted. Firstly, an optimization algorithm based on the optimal received power of EH circuit is proposed, and a power mapping table is constructed through the optimization algorithm, from which the optimal power ratio to meet the user's EH requirements is obtained; then, the nonconvex problem is transformed into a convex problem by using variable substitution, semidefinite relaxation, dichotomous optimization, etc. The simulation results show that compared with the power splitting receiver architecture, the input power threshold range of the QPS receiver architecture is larger, which can avoid the EH circuit falling into the saturated working area and maintain high network throughput.

Hierarchical‐linked batch‐to‐batch optimization based on transfer learning of synthesis process

AbstractIn this work, a hierarchical‐linked batch‐to‐batch optimization based on transfer learning is proposed to realize the effective optimization of a new synthesis process. Optimization efficiency is especially crucial for batch processes to improve the product quality and maximize the economic benefits. The traditional hierarchical optimization method can achieve a better effect, but it may lead to low efficiency since it requires more iterations. To further improve the optimization efficiency of a new batch process with high operational cost, a hierarchical‐linked batch‐to‐batch optimization based on transfer learning is proposed in this work. By introducing the linkage between hierarchies, the available information transmitting between hierarchies is addressed to assist and accelerate the modelling and optimization process. A performance assessment criterion based on the prior knowledge of similar processes is also proposed to further improve the optimization effect. Finally, the performance of the proposed method is verified through a simulation study of the cobalt oxalate synthesis process.

Privacy-aware optimal load scheduling for energy management system of smart home

The wide deployment of smart meters has brought the significant challenge to privacy protection of residential users. However, the existing rechargeable battery schemes cannot protect the privacy when the consumer power usage is either high or low for a long period. A new energy management scheme for smart home is proposed to enhance the privacy protection ability while ensuring the electricity cost and comfort in the article. This article enhances privacy while addressing the joint optimization problem for smart home. Firstly, an energy management architecture incorporated the rechargeable batteries and renewable resources is constructed for enhancing privacy, where the user’s privacy is enhanced by utilizing the rechargeable batteries as well as the home appliance shifting. Secondly, the incentive probability is defined according to user satisfaction and the participation degree of demand response, and a probabilistic incentive mechanism is introduced to encourage more users to participate in household load schedule. Finally, the power cost, privacy as well as user satisfaction are systematically characterized in the objective function of the load scheduling, which is further decomposed by load classification. Then a hierarchical optimization method is leveraged to solve the optimal problem, maximizing the utilization of both residential users and power provider. The simulation results based on Pecan Street dataset validate the performance of the proposed strategy, and compared with the rechargeable battery schemes, the proposed scheme can save 19.72% of the electricity cost while ensuring privacy.

Two-Stage Data-Driven Evolutionary Optimization for High-Dimensional Expensive Problems.

Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for solving complex and computationally expensive optimization problems. However, most of the existing algorithms converge slowly in the later stage. This article proposes a novel two-stage data-driven evolutionary optimization (TS-DDEO) that meets the requirements of early exploration and later exploitation. In the first stage, a surrogate-assisted hierarchical particle swarm optimization method is used to find a promising area from the entire search space. In the second stage, we propose a best-data-driven optimization (BDDO) method with a strong exploitation ability to accelerate the optimization process. BDDO has a real-time update mechanism for the surrogate model and population and uses a predefined number of ranking-top solutions to update population and surrogates. BDDO combines three surrogate-assisted evolutionary sampling strategies: 1) surrogate-assisted differential evolution sampling; 2) surrogate-assisted local search; and 3) a surrogate-assisted full-crossover (FC) strategy which is proposed to integrate existing best genotypes in the population. Experiments and analysis have validated the effectiveness of the two-stage framework, the BDDO method, and the FC strategy. Moreover, the proposed algorithm is compared with five state-of-the-art SAEAs on high-dimensional benchmark functions. The result shows that TS-DDEO performs better both in effectiveness and robustness.

Hierarchical optimization of reservoir development strategy based on reinforcement learning

Production optimization is to increase the profitability of reservoir development by adjusting the type of wells and the rate of injection and production. Previous methods put both variables at the same level when optimizing and thus they change at the same frequency. However, these two variables belong to different scales, because well types require time-consuming and labor consuming operations such as borehole operations, while rate adjustment don't. Therefore, in this paper, a hierarchical optimization method is proposed to optimally control well types and corresponding rate in production sequence. Specifically, a hierarchical framework is first employed based on reinforcement learning method, with well types as high-level variables and quantities as low-level variables. Then, two hindsight mechanisms, named action hindsight and goal hindsight, are used to achieve stably joint optimization of two-level variables at different frequencies. Our findings from the case study reveal that our approach is highly practical, as it has successfully reduced operational costs by 90% and increased cumulative oil production by 18% within three years when compared to the single-level reinforcement method.

Hierarchical optimization method of space patrol task assignment based on deep neural network and consensus-based bundle algorithm

This research looks towards improving the accuracy and efficiency of orbit transfer speed increment estimation and task assignment for space patrol missions involving multiple platforms. First, we set up a quick prediction model for the increment in orbit transfer velocity. Then, a two-layered hybrid optimization framework is developed, with a deep neural network sub-model as the inner layer to find the optimal speed increase and the consensus-based bundle algorithm as the outer layer to solve the multi-task patrol sequence problem. Finally, the simulation and analysis of the patrol task assignment problem are conducted under two types of scenarios. The results show that the hierarchical optimization method proposed in this paper is three orders of magnitude faster than the traditional optimization method. In addition, the suggested hierarchical optimization method has significant promise in practical applications, where it can produce solutions more quickly to adapt to more complex patrol scenarios.

Quasi-perfect and wideband absorption with gradient non-uniform micro-slit array absorber via a hierarchical optimization method

Gradient micro-slit array absorber, a kind of acoustic metamaterial, made of multiple quarter-wavelength resonators, is holding a promising future in industrial utilization due to its flexible adaptation and simple structure. However, designing a perfect and broadband absorber remains a huge challenge because of the larger number of the design parameters and complicated coupling effect between different resonators. In this study, a hierarchical optimization method is developed for gradient micro-slit array absorber. First, a prior test based on two-slit absorber is conducted using transfer matrix method (TMM) to give directions on designing a multi-slit absorber. And then an original absorber model is constructed in the range of 1000–1800 Hz. For the first step, the optimal width of the slit is obtained through the analysis of the quality factor. For the second step, the spacings between slits is optimized by maximizing absorption and minimizing the total length of the waveguide. After optimization, the absorption performance of the optimized model increased by 15.6% and the length decreased by 46% compared with the original model. Finally, the gradient micro-slit array absorber is experimentally investigated and achieves a quasi-perfect and flat absorption ranging from 1000 Hz to 1800 Hz.