Multi-layer Optimization Research Articles

Self-tuning multi-layer optimization algorithm (STML): An innovative parameter-less approach

Computational intelligence (CI)-based methods offer a practical approach to overcoming the significant challenges posed by analytical and enumeration optimization methods when dealing with complex real-world problems. However, a notable drawback of these algorithms is the need for time-consuming and computationally demanding fine-tuning procedures to achieve optimal performance. This paper proposes a novel parameterless auto-tuning meta-heuristic architecture called the self-tuning multi-layer (STML). The fundamental concept behind this architecture involves a multi-layer structure where the inner layer optimizes the main problem. In contrast, the outer layer utilizes information obtained during the search to fine-tune the performance of the inner layer. This feature eliminates manual fine-tuning, as it can autonomously handle this task. A series of mathematical and benchmark problems were employed to demonstrate the computational prowess of the STML. The results indicate its superiority over other meta-heuristic algorithms. Additionally, the STML showcases robustness, as evidenced by the numerical proximity of results obtained from different independent runs on these benchmark problems.

Enhancing Efficiency in Hybrid Marine Vessels through a Multi-Layer Optimization Energy Management System

Abstract: Configuring green power transmissions for heavy-industry marines is treated as a crucial request in an era of global energy and pollution crises. Following up on this hotspot trend, this paper examines the effectiveness of a modified optimization-based energy management strategy (OpEMS) for a dual proton exchange membrane fuel cells (dPEMFCs)-battery-ultra-capacitors (UCs)-driven hybrid electric vessels (HEVs). At first, the summed power of the dual PEMFCs is defined by using the equivalent consumption minimum strategy (ECMS). Accordingly, a map search engine (MSE) is proposed to appropriately split power for each FC stack and maximize its total efficiency. The remaining power is then distributed to each battery and UC using an adaptive co-state, timely determined based on the state of charge (SOC) of each device. Due to the strict constraint of the energy storage devices’ (ESDs) SOC, one fine-corrected layer is suggested to enhance the SOC regulations. With the comparative simulations with a specific rule-based EMS and other approaches for splitting power to each PEMFC unit, the effectiveness of the proposed topology is eventually verified with the highest efficiency, approximately about 0.505, and well-regulated ESDs’ SOCs are obtained.

Optimization of foreground moment deprojection for semi-blind CMB polarization reconstruction

Upcoming Cosmic Microwave Background (CMB) experiments, aimed at measuring primordial CMB polarization B-modes, require exquisite control of instrumental systematics and Galactic foreground contamination. Blind minimum-variance techniques, like the Needlet Internal Linear Combination (NILC), have proven effective in reconstructing the CMB polarization signal and mitigating foregrounds and systematics across diverse sky models without suffering from foreground mismodelling errors. Still, residual foreground contamination from NILC may bias the recovered CMB polarization at large angular scales when confronted with the most complex foreground scenarios.By adding constraints to NILC to deproject statistical moments of the Galactic emission, the Constrained Moment ILC (cMILC) method has been demonstrated to further enhance foreground subtraction, albeit with an associated increase in overall noise variance. Faced with this trade-off between foreground bias reduction and overall variance minimization, there is still no recipe on which moments to deproject and which are better suited for blind variance minimization. To address this, we introduce the optimized cMILC (ocMILC) pipeline, which performs full automated optimization of the required number and set of foreground moments to deproject, pivot parameter values, and deprojection coefficients across the sky and angular scales, depending on the actual sky complexity, available frequency coverage, and experiment sensitivity. The optimal number of moments for deprojection, before paying significant noise penalty, is determined through a data diagnosis inspired by the Generalized NILC (GNILC) method.Validated on B-mode simulations of the PICO space mission concept with four challenging foreground models, ocMILC exhibits lower Galactic foreground contamination compared to NILC and cMILC at all angular scales, with limited noise penalty. This multi-layer optimization enables the ocMILC pipeline to achieve unbiased posteriors of the tensor-to-scalar ratio, regardless of foreground complexity.

Joint Service Caching, Resource Allocation and Task Offloading for MEC-Based Networks: A Multi-Layer Optimization Approach

To provide reliable and elastic Multi-access edge computing services, one feasible solution is to federate geographically proximate edge servers to form a logically centralized resource pool. Optimization of such systems, however, becomes challenging. In this paper, we study the problem of maximizing users' QoE in a MEC-based network, through jointly optimizing service caching, resource allocation and task offloading decisions. We formulate a mixed-integer nonlinear programming (MINLP) problem for the task and establish its NP-hardness. To tackle it efficiently, we propose a novel two-stage algorithmic solution based on approximation and decomposition theory. The proposed algorithm achieves high system performance while at the same time, ensures all constraints from different layers are satisfied. Meanwhile, the structure of the algorithm also fits the multi-layer optimizing feature, making it suitable to be implemented at different layers. In addition, we propose a distributed and online version of our mechanism with very limited information exchange between MEC servers, and further demonstrate how the cost of service switches from real MEC systems can be incorporated into our framework. We evaluate our mechanisms through simulations with both synthetic and real-world traces, and results indicate they are effective as compared to representative baseline algorithms.

Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework

Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework

Electromagnetic Characteristics and Microwave Absorbing Properties of a Novel Stainless Steel Powder

In order to solve the problem of insufficient oxidation resistance and corrosion resistance of magnetic metal absorbent represented by carbonyl iron powder, a wave absorbing composite material which consists of different filling mass proportions of the atomized electrolytic stainless steel powder and paraffin wax is investigated. The electromagnetic (EM) characteristics and absorbing performance of the proposed composite material were tested with Agilent N5234A vector network analyzer in a frequency range of 2–18GHz. The experimental results show that the real and imaginary parts of the dielectric permittivity of stainless steel powder/paraffin composites will increase with the filling mass fraction in the test frequency band, while the real part of the permeability [Formula: see text] decreases with the increase of the mass fraction, and the imaginary part [Formula: see text] increases with the increase of the mass fraction. At a thickness of 3 mm, with the filling mass fraction increasing from 20 wt.% to 60 wt.%, the peak frequency of reflection loss (RL) moves to low frequency from 17.92 GHz to 16 GHz. Meanwhile, the minimum reflection loss (RL[Formula: see text]) also shows a downward trend, and the RL[Formula: see text] value with a filling mass fraction of 60 wt.% is −8.93[Formula: see text]dB. It can be seen that stainless steel powder shows good microwave absorbing performance, and it has excellent anti-oxidation, and anti-corrosion properties. Based on the excellent performance of stainless steel powder, its absorption performance is expected to be further improved through multi-layer optimization design, so as to meet the needs of electromagnetic protection in harsh environments.

Optimal pricing strategies for distribution system operator in coupled power-transportation system

The power grid and transportation network are coupled by the charging behavior of electric vehicles. Based on the coupled power-transportation network model, this paper first analyzes the effect of the distribution system operator’s (DSO) electricity selling price on guiding the charging behavior of electric vehicles in the transportation network and then builds the DSO’s optimal pricing formulation. Considering the competition between multiple charging network operators (CNOs), this paper establishes a game model between CNOs and solves it iteratively through the best response dynamic method. An approximation method using the elasticity matrix is proposed to speed up the solution by reducing the multi-layer optimization to a single layer one in each iteration, with its effectiveness validated through numerical tests. Furthermore, the paper discusses the issue of the prisoner’s dilemma that arises among CNOs and explores the potential impact of their cooperative strategies on the overall system.

Tri-level hierarchical coordinated control of large-scale EVs charging based on multi-layer optimization framework

Tri-level hierarchical coordinated control of large-scale EVs charging based on multi-layer optimization framework

Multi-layer optimization for QKD and key management networks

The maturity and developments of quantum key distribution (QKD) enable its application for encrypting dense wavelength-division multiplexing networks (DWDMNs), ensuring security in the presence of scalable quantum computers. The potential infrastructure reuse and costly deployment of a QKD network (QKDN) require jointly optimized deployment and utilization. We develop and solve a multi-layer problem formulation that fulfills the required keys of the DWDMN demands with the cost-minimized QKDN and maximized reuse of the present infrastructure. Our approach proves the feasibility of currently available QKD devices to encrypt nation-wide DWDMN with multi-period network planning. Furthermore, we compare static and dynamic routing approaches to quantify the advantage of software-defined networking for key management networks (KMNs). Depending on the network load, our multi-layer optimization shows potential savings between 9% and 45% of QKD devices compared to the baseline. Additionally optimizing the operation with dynamic routing increases the performance further, but the gain is negligible for encrypting DWDMNs.

Optical Limiting Sensor Based on Multilayer Optimization of Ag/VO2 Phase Change Material

Optical Limiting Sensor Based on Multilayer Optimization of Ag/VO₂ Phase Change Material

Trinocular camera self-calibration based on spatio-temporal multi-layer optimization

In stereo vision systems with dynamically rotating cameras, the accuracy of camera self-calibration method is reduced due to the interference of space noise and mismatched features. To address this issue, a new self-calibration method for trinocular camera is proposed. Firstly, to obtain the uniformly distributed and high-quality matched feature points required for initial calibration of camera pose, according to the mapping relationship between the three-view feature matching points, a three-view grid feature support estimator is defined, and a three-view ring matching method with double-layer feature closed-loop verification is designed. Then, according to the projection relationship of spatial feature points, a new heterogeneous cross-projection optimization function based on closed-loop features is established, achieving accurate calibration of trinocular camera system. Comparison experiments of multiple scenes verify the effectiveness of the method, particularly in the low-textured scenes, where the average Sampson error ranged between 1.37e-11 and 0.061 pixel. Furthermore, the proposed method achieves higher calibration accuracy than the comparative method, which can improve the robustness of dynamic rotating cameras under spatial noise conditions.

System metabolic engineering of Escherichia coli W for the production of 2-ketoisovalerate using unconventional feedstock.

Replacing traditional substrates in industrial bioprocesses to advance the sustainable production of chemicals is an urgent need in the context of the circular economy. However, since the limited degradability of non-conventional carbon sources often returns lower yields, effective exploitation of such substrates requires a multi-layer optimization which includes not only the provision of a suitable feedstock but the use of highly robust and metabolically versatile microbial biocatalysts. We tackled this challenge by means of systems metabolic engineering and validated Escherichia coli W as a promising cell factory for the production of the key building block chemical 2-ketoisovalerate (2-KIV) using whey as carbon source, a widely available and low-cost agro-industrial waste. First, we assessed the growth performance of Escherichia coli W on mono and disaccharides and demonstrated that using whey as carbon source enhances it significantly. Second, we searched the available literature and used metabolic modeling approaches to scrutinize the metabolic space of E. coli and explore its potential for overproduction of 2-KIV identifying as basic strategies the block of pyruvate depletion and the modulation of NAD/NADP ratio. We then used our model predictions to construct a suitable microbial chassis capable of overproducing 2-KIV with minimal genetic perturbations, i.e., deleting the pyruvate dehydrogenase and malate dehydrogenase. Finally, we used modular cloning to construct a synthetic 2-KIV pathway that was not sensitive to negative feedback, which effectively resulted in a rerouting of pyruvate towards 2-KIV. The resulting strain shows titers of up to 3.22 ± 0.07g/L of 2-KIV and 1.40 ± 0.04g/L of L-valine in 24h using whey in batch cultures. Additionally, we obtained yields of up to 0.81g 2-KIV/g substrate. The optimal microbial chassis we present here has minimal genetic modifications and is free of nutritional autotrophies to deliver high 2-KIV production rates using whey as a non-conventional substrate.

Hybrid metric-feature mapping based on camera and Lidar sensor fusion

Mapping the surroundings is a challenging task required for mobile robot. Fusion of heterogeneous sensors measurement has gradually been the main focus of robot perception. In this work, the Lidar and camera measurement is fused to obtain robust motion estimation and construct hybrid metric-feature map. By coupling visual information, the powerful tracking ability of visual features is heuristically applied to support the scan matching. With the estimation of Lidar odometry, the camera motion in complicated scene can be guaranteed. Meanwhile, the hybrid map is divided into submaps to improve the mapping efficiency, and each submap is composed of a grid-based metric submap and a group of visual features. Then a multi-stage loop closure strategy is applied to determine the loop candidate. Besides, multi-layer optimization including local bundle adjustment and pose graph optimization is performed to obtain global consistent map. The effectiveness of the proposed method is verified in different scenarios.

Optimization of resonant dielectric multilayer for enhanced fluorescence imaging

In the aim of improving the sensitivity in objective-based total internal reflection fluorescence microscopy (ob-TIRF-M), we recently investigated the use of resonant dielectric multilayer (DM) coated coverslips while considering the experimental constraints of commercial ob-TIRF-M. The imaginary part of the refractive index k of the DM top layer material was then shown to be a key parameter to control during the DM optimization in order to limit the discrepancies between theory and experiment. We therefore report here on the development, fabrication and implementation of a specific top layer with a controlled k. This latter was first investigated as a single layer to accurately define its optical properties while adjusting the stoichiometric conditions during deposition. The DM optimization method was then fully revised to include its optical parameters. By doing so, the optimization was freed from the strong dependence on k and we observed a good agreement between the expected and measured fluorescence signal enhancement. This was illustrated by numerically calculating the fluorescence signal enhancement as a function of the top layer k and by comparing it to fluorescent beads imaging over three DM of different k.

Design and Multiobjective Optimization of a Double-Stator Axial Flux SRM With Full-Pitch Winding Configuration

In this article, a novel axial flux double-stator switched reluctance motor (AFDSSRM) is presented and optimized for electric vehicle applications. AFDSSRM adopts the axial arrangement of double-stator and inner rotor structure with a full-pitch winding configuration. The flux generated by the two stators cancels each other at the unaligned position, and then, a low unaligned inductance barely affected by saturation is achieved, which is the primary advantage of the AFDSSRM. First, the topology and power equation of the motor are presented briefly. Due to a large number of dimensional parameters of the proposed structure, comprehensive sensitivity analysis is used to classify the design parameters into strong- and weak-sensitive classes, and a multilayer optimization approach is adopted for the variables of both classes. The response surface method combined with the multiobjective genetic algorithm is employed to optimize the strong-sensitive variables, while the Taguchi algorithm is applied to optimize the weak-sensitive variables. Moreover, the 3-D finite element model is established to analyze the electromagnetic characteristics of the motor. Finally, a prototype motor is manufactured, and the experimental results verify the effectiveness of the proposed structure and the optimization method.

RETRACTED ARTICLE: A novel approach to QoS-aware resource allocation in NOMA cellular HetNets using multi-layer optimization

RETRACTED ARTICLE: A novel approach to QoS-aware resource allocation in NOMA cellular HetNets using multi-layer optimization

Online environmentally adaptive trajectory planning for rotorcraft unmanned aerial vehicles

PurposeThis paper aims to propose an environmentally adaptive trajectory planning system considering the dynamic characteristics of unmanned aerial vehicles (UAVs) and the distance between obstacles and the UAV. The system generates a smooth and safe flight trajectory online.Design/methodology/approachFirst, the hybrid A* search method considering the dynamic characteristics of the quadrotor is used to find the collision-free initial trajectory. Then, environmentally adaptive velocity cost is designed for environment-adaptive trajectory optimization using environmental gradient data. The proposed method adaptively adjusts the autonomous flight speed of the UAV. Finally, the initial trajectory is applied to the multi-layered optimization framework to make it smooth and dynamically viable.FindingsThe feasibility of the designed system is validated by online flight experiments, which are in unknown, complex situations.Practical implicationsThe proposed trajectory planning system is integrated into a vision-based quadrotor platform. It is easily implementable onboard and computationally efficient.Originality/valueA hybrid A* path searching method is proposed to generate feasible motion primitives by dispersing the input space uniformly. The proposed method considers the control input of the UAV and the search time as the heuristic cost. Therefore, the proposed method can provide an initial path with the minimum flying time and energy loss that benefits trajectory optimization. The environmentally adaptive velocity cost is proposed to adaptively adjust the flight speed of the UAV using the distance between obstacles and the UAV. Furthermore, a multi-layered environmentally adaptive trajectory optimization framework is proposed to generate a smooth and safe trajectory.

The multilayer structure design of magnetic-carbon composite for ultra-broadband microwave absorption via PSO algorithm

In this work, the multilayer magnetic-carbon composites were precisely constructed by the Particle Swarm Optimization algorithm to achieve an ultra-broadband electromagnetic wave absorbing (EMA) performance. Composite with different content of magnetic Fe3S4 hollow microspheres and carbon nanotubes were prepared as absorbing fillers to obtain four basic monolayer EMA materials. Based on their different electromagnetic parameters, various multilayer structures were designed and further simulated via the finite element method. In a series of designed EMA materials with intense absorption and broad bandwidth, a 2-layer model with 3.8 mm thickness was selected as an optimal structure, owning to its excellent simulation EMA property with a minimum reflection loss of −40 dB and an extended qualified bandwidth value of 11.7 GHz. According to the designed structural parameters, the 2-layer composite was successfully prepared and the measured results were in good agreement with the simulated properties, confirming the accuracy of the multilayer optimization. The FEM simulation analysis revealed that the high EMA performance of the multilayer composites is attributed to the enhancement of the electromagnetic loss capability as well as the improvement of broadband impedance matching between the material surface and air.

A hierarchical multi-objective capacity planning method for distributed energy system considering complementary characteristic of solar and wind

Distributed energy system (DES) integrated with renewable energy (RE) has attracted extensive attentions and developed rapidly for its high energy utilization rate and sustainability. Both solar and wind, as typical renewable energy, are usually considered in researches on DES while the complementary fluctuation characteristic of solar and wind is seldom taken into optimization objective during the planning process. This paper introduces a quantitative evaluation index of the complementary characteristics of renewable energies and proposes a multi-objective capacity planning method for DES to minimize the economic cost and to maximize the complementarity rate of fluctuation (CROF) simultaneously. To solve the established multi-objective model, a hierarchical optimization framework is employed, which remodels the multi-objective model into multi-layer optimization model by reconstructing new constraints. The nonlinear part of optimization model is linearized by linearization technique. Finally, branch and bound algorithm is employed to obtain the solution to optimization model. Finally, case studies based on typical daily load characteristic curves in three kinds of seasons were performed and analyzed. The results verify the feasibility and effectiveness of proposed method, indicate that the complementarity rate of fluctuation can be improved by 53%, and the average relative fluctuation rate of power purchased from grid can be reduced by 58.7% compared with single economic objective model.

A High Efficiency Transmission Architecture for Electric Vehicles

<div class="section abstract"><div class="htmlview paragraph">A Dual Power Split Electronic Continuously Variable Transmission (DPS-ECVT) with an input-split, output coupled, split-power-path configuration is proposed for improving overall system efficiency and range for electric vehicles. By modulating the power split ratio between the mechanical (planetary gear meshes) and electrical (Motor Generator Units) driveline components, a continuous range of gear ratios operating at higher efficiency is obtained. The proposed concept leverages two power-split units that lead to significantly reduced power flow through the electrical drivelines (compared with single speed EV transmissions as well as single power-split E-CVTs) while providing the same overall ratio spread for transmission operation. A multi-layered optimization is performed, first an inner layer optimization on the operational control strategy to maximize the end-of-cycle SOC (State of Charge) of the battery for a given set of transmission design parameters, and then subsequently an outer layer optimization on the design parameters to maximize the overall efficiency over multiple configurations of the given architecture. The performance of the architecture is bench-marked using simulation models derived from a Chevy Bolt baseline that are simulated on the EPA highway (HWFET) and the urban (UDDS) driving cycles. An effective gain of 13.6 percent in the range is demonstrated. A stick-diagram schematic realization of the optimized configuration is also presented.</div></div>