Advanced Optimization Methods Research Articles

Research on Design Method for Optimal Flow Rate of U-shaped Geothermal Well Based on Exergy Analysis

Closed-loop U-shaped geothermal wells show great potential owing to their special well-depth structure, which can provide a good flow rate and heat extraction. However, no advanced process parameter optimization method is available for U-shaped geothermal wells. To effectively describe the heat transfer processes of U-shaped geothermal wells, an analytical solution that couples transient heat conduction in the surrounding soil (or rocks) with the quasisteady heat transfer process in boreholes was developed. This modelling approach depends on many common elements, such as the thermophysical properties of the working fluid and series of resistances for various elements in the wellbore. Subsequently, based on the exergy analysis method, the optimal operating flow rate was defined and a design method for the optimal flow rate was developed. Results indicate that to obtain the maximum exergy efficiency, different optimal flow rates for the U-shaped geothermal well are achieved at different stages of the heating period. This findings of this study expand the research ideas of the process parameter optimization of U-shaped geothermal wells and provide a theoretical basis for developing an optimal circulating-flow-rate design for U-shaped geothermal wells.

Seamless Parametrization of Spheres with Controlled Singularities

AbstractWe present a method for constructing seamless parametrization for genus‐0 surfaces, which can handle any feasible cone configuration, thus allowing users to arbitrarily design and tailor a mapping as desired. The method directly constructs a self‐overlapping metapolygon of the domain boundary of the mapped cut mesh, which relieves the need of using an auxiliary surface. This simplifies the pipeline and allows for a necessary optimization of the boundary polygon before mapping the interior. Moreover, it enables handling larger meshes with more cones than previous methods can handle. Our construction is purely combinatorial, and it guarantees that the mapping is locally injective – a prerequisite to today's advanced optimization methods. This is achieved via careful construction of a simple domain boundary polygon, where existence of such a polygon is proven for all cases. We offer a numerically robust algorithm to automate the construction, which involves a solution of two linear problems. We offer a full pipeline, suggesting elegant solutions to sub‐problems, and demonstrate robustness through extensive experiments.

Multi-objective optimization with thermodynamic analysis of an integrated energy system based on biomass and solar energies

The integrated energy system (IES) can effectively utilize the distributed renewable energies, but its performance improvement requires advanced design and optimization methods. This study proposes a new IES based on biomass gasification integrated with an internal combustion engine, photovoltaic and solar collectors, an absorption chiller/heater, a heat pump and a thermal storage unit to utilize renewable energies effectively. Based on the thermodynamic models of components considering off-design conditions, a multi-objective optimization model of biomass IES is established to improve the comprehensive performances of energy, economy, and environment. A decision method is proposed to select the optimal configuration considering the Euclidean distance between the Pareto solution and the ideal solution. The results demonstrate that in the specific case, the capacity of the internal combustion engine with biomass gasification is preferred to be low, and 71.4% of Pareto solutions is less than 200 kW during the set range from 20 to 1,000 kW. The larger capacity of solar photovoltaic panels achieves more benefits than solar heat collectors. Moreover, the optimal IES determined by the minimum Euclidean distance has primary energy utilization efficiency of 43.4% and exergy efficiency of 18.0%, which saves primary energy of 7.1%, the annual total cost of 7.2%, and reduces carbon dioxide emission by 41.5%.

A novel machine learning-based electricity price forecasting model based on optimal model selection strategy

Current electricity price forecasting models rely on only simple hybridizations of data preprocessing and optimization methods while ignoring the significance of adaptive data preprocessing and effective optimization and selection strategies to obtain optimal models that improve the forecasting performance. To solve these problems, this study develops an improved electricity price forecasting model that offers the advantages of adaptive data preprocessing, advanced optimization method, kernel-based model, and optimal model selection strategy. Specifically, the adaptive parameter-based variational mode decomposition technology is proposed to provide desirable data preprocessing results, and a leave-one-out optimization strategy based on the chaotic sine cosine algorithm is proposed and applied to develop optimal kernel-based extreme learning machine models. In addition, a newly proposed optimal model selection strategy is applied to determine the developed model that provides the most desirable forecasting result. Numerical results show that the developed model's performance metrics were best, and the average values of mean absolute error, root mean square error, mean absolute percentage error, index of agreement, and Theil's inequality coefficient in four datasets are 0.5121, 0.7607, 0.5722%, 0.9997 and 0.0041, respectively, which imply that the developed model is a promising, applicable and effective electricity price forecasting technique in the real electricity market.

Review of conceptual and systematic progress of precision irrigation

Precision irrigation, defined as accurate and appropriate agricultural techniques characterized by optimal management and best collaboration of various irrigation factors, attracts great attention and obtains wide employments in different irrigation conditions or cultivation processes. Moreover, it becomes well-established in major areas of agricultural researches and across the broad spectrum of agricultural techniques especially in specific sectors of scientific frontiers, including soil quality, irrigation scheduling, water resource distribution, crop productivity, tillage management, climate adaptation, and environment monitoring, etc. This paper reviews the research developments and integrated applications of precision irrigation in typical domains of mechanism and performance, covering key aspects such as process optimization, schedule modelling, and effectiveness evaluation, indicating that advanced irrigation optimization methods support higher productivity of crop field and better environmental conditions of soil; Current schedule modelling techniques provide a set of instructive demonstrations and heuristic descriptions for the working principles of precision irrigation and the quantitative assessments of irrigation productivity; The novel investigation on effectiveness evaluation is extremely significant to obtain higher infiltration efficiency, simultaneously to achieve the optimized irrigation qualities for water balance condition, soil water redistribution, and soil moisture uniformity so that the effectiveness quality of irrigation infiltration could be improved remarkably. It is concluded that precision irrigation owns an outstanding collaborating capability and possesses much better working advancement in typical calibration indexes of cultivation accuracy and infiltration efficiency, meanwhile, a high agreement between the predicted and actual irrigation effectiveness could be expected. This novel irrigation review concentrating on the conceptual and systematic progress should be promoted constructively to improve the quality uniformity for precision irrigation and its constructive influences in different applications, and to facilitate the integrated management of agricultural production by higher irrigation efficiency consequently. Keywords: precision irrigation, process optimization, schedule modelling, effectiveness evaluation, conceptual and systematic progress DOI: 10.25165/j.ijabe.20211404.5463 Citation: Liang Z W, Liu X C, Xiao J R, Liu C H. Review of conceptual and systematic progress of precision irrigation. Int J Agric & Biol Eng, 2021; 14(4): 20–31.

Multiparametric Analysis of a Gravity Retaining Wall

The design of a gravity retaining wall should be simple to construct, quick to build and the best economic solution to a problem. This can be achieved by using advanced optimization methods. Since geotechnical engineers are not always able to determine the exact soil properties and other project data, an optimal design of a gravity retaining wall should also be determined for a wide range of input parameters. Therefore, a multiparametric analysis of an optimal designed gravity retaining wall was carried out. Optimum designs of gravity retaining walls were obtained for 567 combinations of different design parameters. Diagrams were developed to help engineers determine the optimum section of the wall, based on construction costs. An exhaustive search was carried out within the available parameters (project data). The parameters were ranked according to which had the most influence on the optimum cost of the gravity retaining wall and the utilization of multiple constraints. The most important parameter for the optimal cost of a gravity retaining wall is the height of the retained ground, followed by the shear angle of the soil, the soil–wall interaction coefficient, the slope angle and the variable surcharge load. The shear angle of the soil is most relevant to the bearing capacity and eccentricity condition, while the soil–wall interaction coefficient is most relevant to the sliding condition. Since European countries apply different load, material and resistance safety factors, the optimization model was developed in a general form, where different design approaches and unit prices could be applied. The case study provides an improved optimization model for selecting the optimal design of gravity walls, for engineers.

Gradient-based optimization with ranking mechanisms for parameter identification of photovoltaic systems

Deriving optimal photovoltaic (PV) models’ optimal parameters have tremendous significance in simulating, evaluating, and controlling the photovoltaic systems. Determining unknown parameters of these PV models is a multimodal, nonlinear, and complex optimization problem. Hence, developing a robust optimization model to achieve optimal parameters of the PV models effectively is essential. This paper proposes an enhanced metaphor-free gradient-based optimizer (EGBO) for extracting PV parameters quickly, precisely, and reliably. In the EGBO, a rank-based mechanism is employed to update its parameters efficiently. Also, the logistic map (LC) is implemented to better use the local escaping operator (LEO) in the original GBO algorithm. The proposed EGBO optimally identifies various parameters in the PV model, such as single diodes, double diodes, and PV modules. The relevant results indicate that compared with most advanced optimization methods, the EGBO algorithm is competitive in reliability, accuracy, and convergence speed. Moreover, the relevant results from the experimental data drawn from the manufacturer’s datasheet demonstrate that the developed approach can offer highly accurate solutions at various irradiances and temperatures. Consequently, the achieved results confirm that the novel approach can be presented as a utility tool for deriving optimal PV models’ optimal parameters, and it can be helpful in modeling PV systems.

Adaptive network traffic control with an integrated model-based and data-driven approach and a decentralised solution method

This paper presents an adaptive traffic controller for stochastic road networks with an integrated model-based and data-driven solution framework. The model-based optimisation component operates based upon an underlying kinematic wave model driven by stochastic demand within a prediction horizon. The data-driven optimisation component operates based upon an approximate dynamic programming (ADP) formulation which approximates the state-control interactions over future stages with a parametric approximator. The approximator reduces the computational complexity of the adaptive control problem by parameterising the state and decision space. The parametric approximator is to be iteratively updated with online feeding of realisations of traffic states via a temporal difference (TD) learning process. Our results reveal that incorporation of the model-based component facilitates the training of the ADP-based state approximator, and hence improve the overall performance of the control system. We further develop a decentralised solution approach in which individual intersections are allowed to derive their own control policies in an asynchronous manner. The data-driven ADP approximator would serve as a central agent coordinating the control policies derived at individual intersections in the network. This is shown to be able to improve and stabilise the performance of the overall control system even under congested conditions. This is a significant progress in adaptive control system design with use of decentralised optimisation techniques. The present study contributes to the adaptive network traffic control with uncertainties through use of advanced modelling and optimisation methods.

A two-stage Multi-Criteria Optimization method for service placement in decentralized edge micro-clouds

Community networks are becoming increasingly popular due to the growing demand for network connectivity in both rural and urban areas. Community networks are owned and managed at the edge by volunteers. Their irregular topology, the heterogeneity of resources and their unreliable behavior claim for advanced optimization methods to place services in the network. In particular, an efficient service placement method is key for the performance of these systems. This work presents the Multi-Criteria Optimal Placement method, a novel and fast two-stage multi-objective method to place services in decentralized community network edge micro-clouds. A comprehensive set of computational experiments is carried out using real traces of Guifi.net, which is the largest production community network worldwide. According to the results, the proposed method outperforms both the random placement method used currently in Guifi.net and the Bandwidth-aware Service Placement method, which provides the best known solutions in the literature, by a mean gap in bandwidth gain of about 53% and 10%, respectively, while it also reduces the number of resources used.

Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast

The use of data collectors in energy systems is growing more and more [...]

Fixed Point Strategies in Data Science

The goal of this paper is to promote the use of fixed point strategies in data science by showing that they provide a simplifying and unifying framework to model, analyze, and solve a great variety of problems. They are seen to constitute a natural environment to explain the behavior of advanced convex optimization methods as well as of recent nonlinear methods in data science which are formulated in terms of paradigms that go beyond minimization concepts and involve constructs such as Nash equilibria or monotone inclusions. We review the pertinent tools of fixed point theory and describe the main state-of-the-art algorithms for provably convergent fixed point construction. We also incorporate additional ingredients such as stochasticity, block-implementations, and non-Euclidean metrics, which provide further enhancements. Applications to signal and image processing, machine learning, statistics, neural networks, and inverse problems are discussed.

Review of conceptual and systematic progress of precision irrigation

Precision irrigation, defined as accurate and appropriate agricultural techniques characterized by optimal management and best collaboration of various irrigation factors, attracts great attention and obtains wide employments in different irrigation conditions or cultivation processes. Moreover, it becomes well-established in major areas of agricultural researches and across the broad spectrum of agricultural techniques especially in specific sectors of scientific frontiers, including soil quality, irrigation scheduling, water resource distribution, crop productivity, tillage management, climate adaptation, and environment monitoring, etc. This paper reviews the research developments and integrated applications of precision irrigation in typical domains of mechanism and performance, covering key aspects such as process optimization, schedule modelling, and effectiveness evaluation, indicating that advanced irrigation optimization methods support higher productivity of crop field and better environmental conditions of soil; Current schedule modelling techniques provide a set of instructive demonstrations and heuristic descriptions for the working principles of precision irrigation and the quantitative assessments of irrigation productivity; The novel investigation on effectiveness evaluation is extremely significant to obtain higher infiltration efficiency, simultaneously to achieve the optimized irrigation qualities for water balance condition, soil water redistribution, and soil moisture uniformity so that the effectiveness quality of irrigation infiltration could be improved remarkably. It is concluded that precision irrigation owns an outstanding collaborating capability and possesses much better working advancement in typical calibration indexes of cultivation accuracy and infiltration efficiency, meanwhile, a high agreement between the predicted and actual irrigation effectiveness could be expected. This novel irrigation review concentrating on the conceptual and systematic progress should be promoted constructively to improve the quality uniformity for precision irrigation and its constructive influences in different applications, and to facilitate the integrated management of agricultural production by higher irrigation efficiency consequently. Keywords: precision irrigation, process optimization, schedule modelling, effectiveness evaluation, conceptual and systematic progress DOI: 10.25165/j.ijabe.20211404.5463 Citation: Liang Z W, Liu X C, Xiao J R, Liu C H. Review of conceptual and systematic progress of precision irrigation. Int J Agric & Biol Eng, 2021; 14(4): 20–31.

GreedyCenters: Satellite imagery adaptive sampling method for artificial neural networks training

The one of many significant particularities of satellite imagery is large size of images within orders of magnitude exceeds capability of modern GPGPU to train neural networks on its full size. On the other hand satellite imagery tends to be limitedly available. Moreover, the objects of interest tends to constitute a small fraction of whole dataset. This leads to the demand of sample extraction and augmentation method specialized on satellite imagery. Yet this area is immensely underrated so almost all widely used method limited to grid-based sample extraction and augmentation via combinations of 90-degrees rotations and mirroring on vertical or horizontal axes. This paper proposes the domain-agnostic method of sample extraction and augmentation. Adoption of this method to specific subject area is based on domain-specific way to generate significance field of image. In contrast to trivial greedy solutions and more advanced stochastic optimization methods the design of proposed method is focused on maximizing per-step progress. This makes its performance reasonably good even without low-level optimizations without significant quality loss. It can be easily implemented using widely known and open source software libraries.

Optimal Battery Energy Storage System Scheduling Based on Mutation-Improved Grey Wolf Optimizer Using GPU-Accelerated Load Flow in Active Distribution Networks

In this paper, a novel Mutation-Improved Grey Wolf Optimizer (MIGWO) model is introduced in order to solve the optimal scheduling problem for battery energy storage systems (BESS), considering the mass integration of renewable energy sources (RES), such as solar and wind generation, in active distribution networks. In this regard, four improvements are applied to the conventional GWO algorithm to modify the exploration–exploitation balance for an enhanced convergence rate. The validity and performance of the proposed model are tested on 23 classical benchmark functions and compared to the original algorithm. The new technologies present in active distribution networks lead to increased complexity in the efficient coordination of existing resources, making it necessary to resort to advanced optimization and calculation methods. As operational planning and control functions in power systems are computationally demanding and require multiple power flow calculations, the necessity of simultaneous (parallel) computing techniques emerged. In order to reduce the computing time, an accelerated GPU parallel computing technique is also applied in the proposed model. The MIGWO algorithm is further applied on the modified IEEE-33 bus system aiming to minimize the total power losses, based on the optimal coordination of BESS operation scheduling and RES generation for multiple load demand and local generation scenarios, as well as for various initial state-of-charge values of BESS.

PID Controller Gains Tuning Using Metaheuristic Optimization Methods: A survey

In nowadays industry, most processes are controlled and automated. Interestingly, PID controllers are major contributors to the control process since they were invented and become quite practical. PID controllers are vital component in the industry and enhancing the component will show an echo effect in today’s technology. Their drawbacks are tuning them for an application, and this provides inspiration to develop advanced optimization methods in tuning PID controllers. This survey aims to review metaheuristic optimization methods of PID controller tuning that were published between 2010 and 2018. The paper was constructed based on 22 research papers and found that 8 metaheuristics optimization methods were used with PID tuning on 5 industrial applications. The papers also extensively provided answers to 3 research questions and assessing the quality of the papers based on 6 parameters.

Hierarchical, Grid-Aware, and Economically Optimal Coordination of Distributed Energy Resources in Realistic Distribution Systems.

Renewable portfolio standards are targeting high levels of variable solar photovoltaics (PV) in electric distribution systems, which makes reliability more challenging to maintain for distribution system operators (DSOs). Distributed energy resources (DERs), including smart, connected appliances and PV inverters, represent responsive grid resources that can provide flexibility to support the DSO in actively managing their networks to facilitate reliability under extreme levels of solar PV. This flexibility can also be used to optimize system operations with respect to economic signals from wholesale energy and ancillary service markets. Here, we present a novel hierarchical scheme that actively controls behind-the-meter DERs to reliably manage each unbalanced distribution feeder and exploits the available flexibility to ensure reliable operation and economically optimizes the entire distribution network. Each layer of the scheme employs advanced optimization methods at different timescales to ensure that the system operates within both grid and device limits. The hierarchy is validated in a large-scale realistic simulation based on data from the industry. Simulation results show that coordination of flexibility improves both system reliability and economics, and enables greater penetration of solar PV. Discussion is also provided on the practical viability of the required communications and controls to implement the presented scheme within a large DSO.

Joint Radio Resource Allocation and Cooperative Caching in PD-NOMA-Based HetNets

In this paper, we propose a novel joint resource allocation and cooperative caching scheme for power-domain non-orthogonal multiple access (PD-NOMA)-based heterogeneous networks (HetNets). In our scheme, the requested content is fetched directly from the edge if it is cached in the storage of one of the base stations (BSs), and otherwise is fetched via the backhaul. Our scheme consists of two phases: 1) Caching phase where the contents are saved in the storage of the BSs; and 2) Delivery phase where the requested contents are delivered to users. We formulate a novel optimization problem over radio resources and content placement variables. We aim to minimize the network cost subject to quality-of-service (QoS), caching, subcarrier assignment, and power allocation constraints. By exploiting advanced optimization methods, such as alternative search method (ASM), Hungarian algorithm, successive convex approximation (SCA), we obtain an efficient sub-optimal solution of the optimization problem. Numerical results illustrate that our ergodic caching policy via the proposed resource management algorithm can achieve a considerable reduction on the total cost on average compared to the most popular caching and random caching policy. Moreover, our cooperative NOMA scheme outperforms orthogonal multiple access (OMA) in terms of the delivery cost in general with an acceptable complexity increase.

Energy-Efficient Wearable EPTS Device Using On-Device DCNN Processing for Football Activity Classification

This paper presents an energy-optimized electronic performance tracking system (EPTS) device for analyzing the athletic movements of football players. We first develop a tiny battery-operated wearable device that can be attached to the backside of field players. In order to analyze the strategic performance, the proposed wearable EPTS device utilizes the GNSS-based positioning solution, the IMU-based movement sensing system, and the real-time data acquisition protocol. As the life-time of the EPTS device is in general limited due to the energy-hungry GNSS sensing operations, for the energy-efficient solution extending the operating time, in this work, we newly develop the advanced optimization methods that can reduce the number of GNSS accesses without degrading the data quality. The proposed method basically identifies football activities during the match time, and the sampling rate of the GNSS module is dynamically relaxed when the player performs static movements. A novel deep convolution neural network (DCNN) is newly developed to provide the accurate classification of human activities, and various compression techniques are applied to reduce the model size of the DCNN algorithm, allowing the on-device DCNN processing even at the memory-limited EPTS device. Experimental results show that the proposed DCNN-assisted sensing control can reduce the active power by 28%, consequently extending the life-time of the EPTS device more than 1.3 times.

An advanced virtual synchronous generator control technique for frequency regulation of grid-connected PV system

The future power system is developing to an inverter-based system from a machine dominated power system due to a large integration of renewable energy sources (RESs). Lack of inertial part and intermittency of the RESs causes frequency instability in the power system. The virtual synchronous generator (VSG) development has been made to add inertial control to the power system for controlling the frequency deviation caused by the integration of the photovoltaic system. The VSG system comprised of power electronics components having a droop control mechanism like the conventional synchronous generator, which provides the necessary frequency stability to the power system. In this paper, three parallel VSG based photovoltaic systems integrated with battery storage systems are used to analyze the frequency response and its stability. Moreover, an advanced genetic algorithm optimization method is used to get the optimal values of parameters of the droop controller used in VSG. Furthermore, two different cases with respect to variation in supply and load power of the system are analyzed in detail. Additionally, an IEEE 14 bus system is utilized to validate the proposed system in MATLAB/Simulink. Evident improvements are observed in the simulation results presented through the suggested GA based VSG control technique.

An effective topology optimization method for crashworthiness of thin-walled structures using the equivalent linear static loads

The equivalent static loads method for nonlinear dynamic response structural optimization may be failed in large deformation crash conditions, due to topology optimization with the equivalent static loads mostly beyond the linear range and causing numerical defects such as high compliance of elements. To overcome the above disadvantage, an advanced structural topology optimization method for crashworthiness considering crash-reduced large deformation and plastic buckling is proposed using newly defined equivalent linear static loads. The equivalent linear static loads can adaptively scale to guarantee that the topology optimization is performed within linear range. At each cycle, the crash simulation is performed and the nonlinear nodal displacement vector at the time step with the maximum strain energy is scaled by an adaptive displacement-scaling factor. The equivalent linear static loads that are generated by multiplying the linear stiffness matrix and the scaled nodal displacement vector will be incorporated into topology optimization, which can guarantee the topology optimization to remain in linear range and further solve the numerical instability problems. The process is repeated until the convergence criteria are satisfied. The effectiveness of the proposed method is evaluated by solving a crashworthiness topology optimization of a crash box considering crash-induced plastic buckling to determine the location and profile of crash triggers. The results show that the proposed method can effectively solve the large deformation crashworthiness topology optimization of thin-walled structures and provides a feasible strategy for crash triggers design in crash box.