General Optimization Research Articles

Artificial Bee Colony Based Iterative Heuristic Signal Detection Technique for Optical Wireless Communications

In this paper, we consider a problem of optical signal detection and present a novel artificial bee colony (ABC) based heuristic approach for optical spectrum sensing in a free-space optical wireless network that overcomes channel impairment due to noise uncertainty. In particular, we propose a generic ABC-based optimization framework in conjunction with eigenvalue-based detection (EVD). The uniqueness of the proposed algorithm is that it is executed iteratively by comparing various local solutions until an optimum solution is obtained. The channel is characterized as correlated multivariate fading and the received signal is quantified as a continuous waveform wideband detector. We derive the joint statistic of the correlated multivariate channel fading in the multiple-input multiple-output (MIMO) optical network. Subsequently, the marginal probability density function of the channel coefficients is obtained. For comparison purposes with existing optical spectrum sensing techniques, noise uncertainty is considered. It is demonstrated from simulations that the proposed ABC-EVD spectrum sensing yields performance improvement and the analytical results derived are verified. From the comparative study, it is also found that the proposed scheme outperforms existing optical spectrum sensing techniques, particularly in a regime of low signal-to-noise ratio. When compared to EVD, it is shown that, at a received signal-to-noise ratio of -15 dB, the performance gain achieved in terms of the probability of detection is 15% higher over the entire range of target probability of false alarm. When compared with the ED technique, the proposed technique outperforms it with approximately 50% higher probability of detection at the noise uncertainty of 1 dB and received SNR of -10 dB. The proposed algorithm offers fast convergence, strong robustness, and high flexibility.

Iterative optimization method for determining optimal shape parameter in RBF-FD method

In this paper, we propose a novel iterative optimization method to determine the best parameter c for the Radial basis function finite difference (RBF-FD) method based on the Double Operator Error (DOE). This method is a general iterative optimization approach that can rapidly determine the optimal c for a given problem and reduce the numerical error significantly, for any type of radial basis functions (RBFs). Three numerical examples are presented to show the effectiveness and generality of the proposed method over previous methods. The relevant data and code can be found at https://github.com/hsbhc/IOM-RBF-FD.

On Geometric Connections of Embedded and Quotient Geometries in Riemannian Fixed-Rank Matrix Optimization

In this paper, we propose a general procedure for establishing the geometric landscape connections of a Riemannian optimization problem under the embedded and quotient geometries. By applying the general procedure to the fixed-rank positive semidefinite (PSD) and general matrix optimization, we establish an exact Riemannian gradient connection under two geometries at every point on the manifold and sandwich inequalities between the spectra of Riemannian Hessians at Riemannian first-order stationary points (FOSPs). These results immediately imply an equivalence on the sets of Riemannian FOSPs, Riemannian second-order stationary points (SOSPs), and strict saddles of fixed-rank matrix optimization under the embedded and the quotient geometries. To the best of our knowledge, this is the first geometric landscape connection between the embedded and the quotient geometries for fixed-rank matrix optimization, and it provides a concrete example of how these two geometries are connected in Riemannian optimization. In addition, the effects of the Riemannian metric and quotient structure on the landscape connection are discussed. We also observe an algorithmic connection between two geometries with some specific Riemannian metrics in fixed-rank matrix optimization: there is an equivalence between gradient flows under two geometries with shared spectra of Riemannian Hessians. A number of novel ideas and technical ingredients—including a unified treatment for different Riemannian metrics, novel metrics for the Stiefel manifold, and new horizontal space representations under quotient geometries—are developed to obtain our results. The results in this paper deepen our understanding of geometric and algorithmic connections of Riemannian optimization under different Riemannian geometries and provide a few new theoretical insights to unanswered questions in the literature. Funding: X. Li was partially supported by National Key R&D Program of China [Grants 2020YFA0711900, 2020YFA0711901], the National Natural Science Foundation of China [Grants 12271107, 62141407], the Young Elite Scientists Sponsorship Program by CAST [Grant 2019QNRC001], the “Chenguang Program” by the Shanghai Education Development Foundation and Shanghai Municipal Education Commission [Grant 19CG02], the Shanghai Science and Technology Program [21JC1400600]. Y. Luo and A. R. Zhang were partially supported by the National Science Foundation [CAREER-2203741].

A General Analysis and Optimization Framework of Time Index Modulation for Integrated Data and Energy Transfer

Integrated data and energy transfer (IDET) has been considered as a key technology to tackle the energy shortage in the densely connected wireless networks. In this paper, a novel time index modulation (TIM) assisted IDET system is studied, where data information is delivered not only by the conventional modulation scheme, but also by the time index of the activated symbol durations for either wireless data transfer (WDT) or wireless energy transfer (WET). With the aid of theoretical analysis, the average energy harvesting performance as well as the achievable data rate between the transmitter and the receiver are both derived in the semi-closed form. Further, a population-based optimizer is conceived to obtain the power allocation of WDT and WET signals, in order to maximize the energy harvesting performance by satisfying the constraints of the achievable data rate. Simulation results validate our theoretical analysis, while they also demonstrate that the TIM assisted system can substantially increase the IDET performance.

A General Optimization Method for Complex Requirements Antenna Based on Balanced Demotion MOEA

A general Multi-objective evolutionary algorithm (MOEA) is presented to solve the antenna optimization problem with complex requirements. By analyzing the common requirements in the antenna design process, a strategy of calculating the difference between the indicator value and the threshold is applied to construct the objective units that can accurately evaluate the antenna performance and reconstruct the objective functions quickly. The fitness functions can solve the fuzzy requirements problem. An adaptive mechanism is designed for mutation and crossover operation in order to balance the convergence rate and the ability to avoid local extremes. In non-dominated sorting, the crowding calculation method is improved so that the individuals can be dispersed in the objective space. A demotion mechanism is designed for the selection operation to balance the importance of all objective functions and constraints. It can not only make the optimal solution closer to the actual requirement but also reduce the computation cost of the algorithm itself. Then this algorithm is applied to an ultra-wideband (UWB) dipole antenna with complex requirements as a case study. Analyzing the optimization results shows that all the indicators of the antenna optimized by the algorithm can well satisfy the actual requirements.

General optimization of breakdown voltage and resistivity on power components in terms of doping level and thickness

The emergence of wide band-gap (WBG) materials promises improved performance in semiconductor devices, but also presents significant technical challenges. Developing this immature technology and adapting market-ready silicon-based technology to new materials requires a thorough understanding of the relationship between material properties and the final electrical performance of the component. In this work, we propose a model that relates generic material properties and device-specific parameters (such as doping level or thickness) to the final breakdown voltage and performance of unipolar vertical diodes. Our approach enables optimized device design and evaluation by connecting the electrical performance target with the optimal drift layer characteristics. This model can be applied universally to any material with known impact ionization coefficients, carrier mobility, and thermal activation energy. We focus on diamond as a paradigm for WBG materials, illustrating specific characteristics such as incomplete carrier ionization or elevated doping-dependent critical electric fields. Our model predicts that diamond-based vertical devices with 70 μm layers and doping levels of 1.1015 cm−3 can sustain 20 kV.

ИССЛЕДОВАНИЕ УПРАВЛЕНИЯ РАЗВИТИЕМ ЭЛЕКТРОННОЙ КОММЕРЦИИ В СЕЛЬСКИХ РАЙОНАХ КИТАЯ: ПРОБЛЕМЫ И РЕШЕНИЯ

This paper is the second part of the survey . In recent years, e-commerce in rural areas of China has been developing rapidly, which has an impact on regional development. The article explores and analyzes the issues of e-commerce management in rural areas of China. It has previously been identified that this area has problems such as backward infrastructure, an imperfect system of agricultural supply chains, and unbalanced regional development of e-commerce and conditions for its conduct. The article proposes solutions aimed at improving the management of e-commerce development in rural China. The author recommends: a) setting a management priority for reducing the rural e-commerce gap in various regions of China; b) develop a model of rural e-commerce and implement a general optimization scheme for the development of rural e-commerce in the country; c) organize the process of transferring experience and professional knowledge from regions with high development of e-commerce to regions with its weak use. The key management solutions for the development of rural e-commerce in China are: strengthening infrastructure; optimization of the agricultural supply chain system; improving standardization and branding of rural e-commerce goods; balancing regional differences in rural e-commerce development.

Performance Investigation and Experimental Testing of a Stator-PM-Excitation Axial-Flux Magnetic Gear

This paper proposes a stator-excitation axial-flux magnetic gear (SEAMG) based on innovative improvement from rotor excitation to stator excitation for the magnetic gear. The key design of the proposed SEAMG is to place the permanent magnets (PMs) on the stator, aiming at only retaining iron on rotors to enhance its mechanical robustness, and the PMs located on the stator can be avoided falling off to further improve operation reliability. An axial-flux pattern is adopted so as to make that the stator can be easily fixed to the housing and the high- and low-speed rotor shafts can be conveniently led out from axial side. Hence, compared with the existing coaxial rotor-excitation MGs, the proposed SEAMG has improved mechanical reliability and low processing difficulty. Its operation principle is explained and the general optimization design considerations are illustrated to achieve expected torque transmission performance. A prototype is built and performance evaluation using the 3-D finite element analysis (FEA) and the experiments is conducted to verify its validity.

Order release optimisation for time-dependent and stochastic manufacturing systems

Order release optimisation is essential in production planning, especially in discrete manufacturing. Order release planning models with load-dependent lead times must anticipate the time-dependent work-in-process and output for any given release schedule and thus require an anticipation model that approximates the time-dependent behaviour of queueing systems. We present a generic optimisation model for order release planning in stochastic, non-stationary manufacturing systems that includes a well-defined interface for the anticipation model. We develop two stationary backlog carryover (SBC) approaches to approximate time-dependent queueing behaviour and prove their consistency with the order release model. The resulting nonlinear programming model is shown to be a special case of the well-known clearing function models. A numerical study demonstrates that the optimised order releases for different demand patterns are close to the optimum that results from simulation-based optimisation even for extreme demand and release patterns. The resulting output closely matches the simulated output with some deviations.

A multi-task learning-based optimization approach for finding diverse sets of microstructures with desired properties

Optimization along the chain processing-structure-properties-performance is one of the core objectives in data-driven materials science. In this sense, processes are supposed to manufacture workpieces with targeted material microstructures. These microstructures are defined by the material properties of interest and identifying them is a question of materials design. In the present paper, we addresse this issue and introduce a generic multi-task learning-based optimization approach. The approach enables the identification of sets of highly diverse microstructures for given desired properties and corresponding tolerances. Basically, the approach consists of an optimization algorithm that interacts with a machine learning model that combines multi-task learning with siamese neural networks. The resulting model (1) relates microstructures and properties, (2) estimates the likelihood of a microstructure of being producible, and (3) performs a distance preserving microstructure feature extraction in order to generate a lower dimensional latent feature space to enable efficient optimization. The proposed approach is applied on a crystallographic texture optimization problem for rolled steel sheets given desired properties.

Fairness in maximal covering location problems

This paper provides a mathematical optimization framework to incorporate fairness measures from the facilities’ perspective to discrete and continuous maximal covering location problems. The main ingredients to construct a function measuring fairness in this problem are the use of (1) ordered weighted averaging operators, a popular family of aggregation criteria for solving multiobjective combinatorial optimization problems; and (2) α-fairness operators which allow generalizing most of the equity measures. A general mathematical optimization model is derived which captures the notion of fairness in maximal covering location problems. The models are first formulated as mixed integer non-linear optimization problems for both the discrete and the continuous location spaces. Suitable mixed integer second order cone optimization reformulations are derived using geometric properties of the problem. Finally, the paper concludes with the results obtained from an extensive battery of computational experiments on real datasets. The obtained results support the convenience of the proposed approach.

A Genetic Algorithm for Universal Optimization of Ultrasensitive Surface Plasmon Resonance Sensors with 2D Materials.

We present a general optimization technique for surface plasmon resonance, (SPR) yielding a range of ultrasensitive SPR sensors from a materials database with an enhancement of ∼100%. Applying the algorithm, we propose and demonstrate a novel dual-mode SPR structure coupling SPP and a waveguide mode within GeO2 featuring an anticrossing behavior and an unprecedented sensitivity of 1364 deg/RIU. An SPR sensor operating at wavelengths of 633 nm having a bimetal Al/Ag structure sandwiched between hBN can achieve a sensitivity of 578 deg/RIU. For a wavelength of 785 nm, we optimized a sensor as a Ag layer sandwiched between hBN/MoS2/hBN heterostructures achieving a sensitivity of 676 deg/RIU. Our work provides a guideline and general technique for the design and optimization of high sensitivity SPR sensors for various sensing applications in the future.

An optimization framework for enterprise quality infrastructure system under coupling constraints

Improving quality infrastructure (QI) is the cornerstone of improving enterprise quality management activities. Currently, the lack of a QI system optimization approach makes it difficult to exploit the role of QI for enterprise quality improvement. In this study, a generic QI system optimization framework is proposed to guide the development of enterprise QI optimization strategies, and three ideas of coupling constraints are presented in QI optimization for the first time: (1) How does optimization reflect the logical connotation of QI? (2) How to incorporate the cost of optimization? (3) How to determine and guide the direction of QI optimization? Firstly, the QI optimization index system is constructed to reflect the current QI level and future development goals of the enterprise. Secondly, the coupling constraint mechanism of QI system optimization is modeled based on the idea of the heterogeneous information network: the QI indicator sensitivity algorithm is designed to integrate it with cost and urgency. Finally, the optimization index characterizing the priority of QI index optimization is proposed to realize the formulation of enterprise optimization strategies. The framework is applied to eight QI system optimization cases of typical Oil Country Tubular Goods (OCTG) companies. The results show that the proposed framework can solve the three major factors in QI optimization, which is more in line with the enterprise optimization construction and fills the gap in the field of QI optimization. This work can provide ideas for the optimization of systems with such coupling constraint characteristics in other fields.

Virtual reality and collaborative learning: a systematic literature review

Background: While research on Virtual Reality’s potential for education continues to advance, research on its support for Collaborative Learning is small in scope. With remote collaboration and distance learning becoming increasingly relevant for education (especially since the COVID-19 pandemic), an understanding of Virtual Reality’s potential for Collaborative Learning is of importance. To establish how this immersive technology can support and enhance collaboration between learners, this systematic literature review analyses scientific research on Virtual Reality for Collaborative Learning with the intention to identify 1) skills and competences trained, 2) domains and disciplines addressed, 3) systems used and 4) empirical knowledge established.Method: Two scientific databases—Scopus and Web of Science—were used for this review. Following the PRISMA method, a total of 139 articles were analyzed. Reliability of this selection process was assessed using five additional coders. A taxonomy was used to classify these articles. Another coder was used to assess the reliability of the primary coder before this taxonomy was applied to the selected articlesResults: Based on the literature reviewed, skills and competences developed are divided into five categories. Educational fields and domains seem interested in Virtual Reality for Collaborative Learning because of a need for innovation, communities and remote socialization and collaboration between learners. Systems primarily use monitor-based Virtual Reality and mouse-and-keyboard controls. A general optimism is visible regarding the use of Virtual Reality to support and enhance Collaborative LearningConclusion: Five distinct affordances of Virtual Reality for Collaborative Learning are identified: it 1) is an efficient tool to engage and motivate learners, 2) supports distance learning and remote collaboration, 3) provides multi- and interdisciplinary spaces for both learning and collaborating, 4) helps develop social skills and 5) suits Collaborative Learning-related paradigms and approaches. Overall, the reviewed literature suggests Virtual Reality to be an effective tool for the support and enhancement of Collaborative Learning, though further research is necessary to establish pedagogies.

Towards an optimized design for an elevated cyclonic home using numerical simulations and active learning framework

This research presents a general optimization workflow and a geometrical parametric study using computational fluid dynamics (CFD) analyses in conjunction with a Kriging-based active learning strategy. Motivated by the concept of a wind-resistant “cyclonic home”, the present work conducts a systematic numerical wind study for a typical gable-roof elevated house structure across a parameter space consisting of varying incident wind directions and roof pitch angles. The house model is fully parametrized such that geometrical and mesh generation are automatically performed to facilitate the deployment of active learning for establishing a surrogate model of wind-induced forces. A wind force map is produced which reveals the relationship between the incident wind angle and roof pitch. From this, statistical analysis exposes which roof pitch angles offer greater wind-resistance, i.e., a roof pitch of θ=32.9° is found to provide the lowest mean force across all possible wind angles, whereas roof pitches of θ=12.7° and θ=45.0° yield the largest forces. The influence of the overhanging eave, a typical feature of such houses, is varied to understand its role on overall roof performance. Quartering winds are found to provide a greater contribution to overall roof forces as the eave length is extended. In contrast, for winds perpendicular to the roof ridge, its extension provides a relatively stable total force. These findings can assist a variety of stakeholders from engineering and risk assessment disciplines in pursuit of buildings resilient to high-winds due to tropical cyclones.

A Hybrid Brain Storm Optimization Algorithm to Solve the Emergency Relief Routing Model

Due to the inappropriate or untimely distribution of post-disaster goods, many regions did not receive timely and efficient relief for infected people in the coronavirus disease outbreak that began in 2019. This study develops a model for the emergency relief routing problem (ERRP) to distribute post-disaster relief more reasonably. Unlike general route optimizations, patients’ suffering is taken into account in the model, allowing patients in more urgent situations to receive relief operations first. A new metaheuristic algorithm, the hybrid brain storm optimization (HBSO) algorithm, is proposed to deal with the model. The hybrid algorithm adds the ideas of the simulated annealing (SA) algorithm and large neighborhood search (LNS) algorithm into the BSO algorithm, improving its ability to escape from the local optimum trap and speeding up the convergence. In simulation experiments, the BSO algorithm, BSO+LNS algorithm (combining the BSO with the LNS), and HBSO algorithm (combining the BSO with the LNS and SA) are compared. The results of simulation experiments show the following: (1) The HBSO algorithm outperforms its rivals, obtaining a smaller total cost and providing a more stable ability to discover the best solution for the ERRP; (2) the ERRP model can greatly reduce the level of patient suffering and can prioritize patients in more urgent situations.

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In this research paper, a swarm evolutionary modeling technique called the Updated Multi-Objective Ant-Lion Optimizer (UMO-ALO) is applied to the modeling of Open Circuit Voltage (OCV) state-of-charge (SOC) of Lithium-ion cells with cathode-anode composition (LiMnO2/Li4TiO). The model approximates the battery SOC by considering several battery cell internal physical parameters and a linear fitness function governed by two objectives involving the charge and discharge response of the battery cell. Bound limited coefficients of the physical parameters are used in the optimization process. The UMO-ALO uses a special update procedure to reduce the computational run-time of standard MO-ALO and hence speeds up the generic optimizer. Simulations were performed using a very small real laboratory data obtained from the relevant field studies and for different trial-run configurations of UMO-ALO. The results from these simulations show very good fitness responses close to zero margins for both trial-run configurations.

Efficient Calibration of Groundwater Contaminant Transport Models Using Bayesian Optimization.

Numerical modeling is a significant tool to understand the dynamic characteristics of contaminants transport in groundwater. The automatic calibration of highly parametrized and computationally intensive numerical models for the simulation of contaminant transport in the groundwater flow system is a challenging task. While existing methods use general optimization techniques to achieve automatic calibration, the large numbers of numerical model evaluations required in the calibration process lead to high computing overhead and limit the efficiency of model calibration. This paper presents a Bayesian optimization (BO) method for efficient calibration of numerical models of groundwater contaminant transport. A Bayes model is built to fully represent calibration criteria and derive the objective function for model calibration. The efficiency of model calibration is made possible by the probabilistic surrogate model and the expected improvement acquisition function in BO. The probabilistic surrogate model approximates the computationally expensive objective function with a closed-form expression that can be computed efficiently, while the expected improvement acquisition function proposes the most promising model parameters to improve the fitness to the calibration criteria and reduce the uncertainty of the surrogate model. These schemes allow us to find the optimized model parameters effectively by using a small number of numerical model evaluations. Two case studies for the calibration of the Cr(VI) transport model demonstrate that the BO method is effective and efficient in the inversion of hypothetical model parameters, the minimization of the objective function, and the adaptation of different model calibration criteria. Specifically, this promising performance is achieved within 200 numerical model evaluations, which substantially reduces the computing budget for model calibration.

Performance enhanced geometrically shaped 32-ary quadrature amplitude modulation enabled by swarm intelligence algorithm

The geometrically shaped 32-ary quadrature amplitude modulation (GS-32QAM) signal enabled by swarm intelligence algorithm is proposed in high-speed and long-haul coherent optical communication system. The geometric shaping (GS) is obtained by constructing a generic constellation optimization scheme applying particle swarm algorithm (PSO), wolf pack algorithm (WPA), and marine predator algorithm (MPA), respectively. The complexity of three algorithms applied to GS-32QAM is systematically analyzed, where MPA can obtain better search results with slightly higher complexity than PSO. The results show that the optimized constellation is significantly better than the uniform signal in terms of reducing bit error rate and increasing generalized mutual information (GMI). The maximum GMI gain of GS signal with PSO and WPA are both 0.25 bit / sym, and the maximum GMI gain of GS signal with MPA is up to 0.3 bit / sym. At the hard-decision forward error correlation limitation of 3.8 × 10 − 3, compared with uniform signal, the GS signal with PSO, WPA, and MPA can provide the optical signal-to-noise ratio gain of 1, 1.1, and 1.6 dB tolerance, respectively. In terms of data rate, compared with uniform signal, three GS signals can increase by 33, 36, and 43 Gbit / s, respectively.

Striving for Sparsity: On Exact and Approximate Solutions in Regularized Structural Equation Models

Regularized structural equation models have gained considerable traction in the social sciences. They promise to reduce overfitting by focusing on out-of-sample predictions and sparsity. To this end, a set of increasingly constrained models is fitted to the data. Subsequently, one of the models is selected, usually by means of information criteria. Current implementations of regularized structural equation models differ in their optimizers: Some use general purpose optimizers whereas others use specialized optimization routines. While both approaches often perform similarly, we show that they can produce very different results. We argue that in particular, the interaction between optimizer and selection criterion (e.g., BIC) contributes to these differences. We substantiate our arguments with an empirical demonstration and a simulation study. Based on these findings, we conclude that researchers should consider specialized optimizers whenever possible. To facilitate the implementation of such optimizers, we provide the R package lessSEM.