Proposed Optimization Approach Research Articles

A fuzzy interval optimization approach for p-hub median problem under uncertain information.

Stochastic and robust optimization approaches often result in sub-optimal solutions for the uncertain p-hub median problem when continuous design parameters are discretized to form different environmental scenarios. To solve this problem, this paper proposes a triangular fuzzy number model for the Non-Strict Uncapacitated Multi-Allocation p-hub Median Problem. To enhance the quality and the speed of optimization, a novel optimization approach, combining the triangular fuzzy number evaluation index with the Genetic-Tabu Search algorithm, is proposed. During the iterations of the Genetic-Tabu Search algorithm for finding the optimal solution, the fitness of fuzzy hub schemes is calculated by considering the relative positional relationships of triangular fuzzy number membership functions. This approach directly addresses the triangular fuzzy number model and ensures the integrity of information in the p-hub problem as much as possible. It is verified by the classic Civil Aeronautics Board and several self-constructed data sets. The results indicate that, compared to the traditional Genetic Algorithm and Tabu Search algorithm, the Genetic-Tabu Search algorithm reduces average computation time by 49.05% and 40.93%, respectively. Compared to traditional random, robust, and real-number-based optimization approaches, the proposed optimization approach reduces the total cost in uncertain environments by 1.47%, 2.80%, and 8.85%, respectively.

Optimal Energy Storage Configuration of Prosumers with Uncertain Photovoltaic in the Presence of Customized Pricing-Based Demand Response

Setting an acceptable pricing strategy to attract prosumers to participate in demand response and orderly configure energy storage is a critical topic for virtual power plants (VPPs) in improving sustainable development. Based on this, this paper proposes a two-layer iterative optimization to develop a customized pricing-based demand response for energy storage with uncertain photovoltaic (PV) for prosumers. In the upper layer, the VPP formulates a customized price consisting of a two-part electricity price, on-grid electricity price and auxiliary service price according to the load characteristics of prosumers, so as to make the power supply and demand of prosumers more controllable. In the lower layer, prosumers adjust their energy storage configurations and energy consumption behavior according to the price signal, considering the uncertainty of PV. The research shows that the proposed optimization approach can encourages prosumers to configure energy storage, and explore user-side flexibility resources. The full utilization of energy storage has increased the PV output of the prosumers by 10%, and its benefits have also increased by 7%.

Improving the hydraulic performance of a high-speed submersible axial flow pump based on CFD technology

The hydraulic performance of a high-speed submersible axial flow pump is investigated to reduce its energy consumption. A more efficient and stable optimization method that combines parametric design, computational fluid dynamics, and a computer algorithm is proposed. The main aim is to broaden the high-efficiency operating zone, so the average efficiency under multiple conditions is optimized while considering rotor–stator matching. The design-of-experiments method and a radial-basis-function neural network are combined to form the optimization platform, and automatic optimization of the pump design is realized through repeated execution of design and simulation. The flow loss mechanism inside the pump is studied in depth via the entropy generation rate, and regression analysis shows that the pump efficiency is influenced mainly by the blade angles. After optimization, the target efficiency is increased by 8.34%, and the flow field distribution shows that the channel vortex and hydraulic loss are controlled effectively. Finally, the results are validated by experiment. The proposed optimization approach has advantages in saving manpower and obtaining globally optimal solutions.

Maximizing acoustic band gap in phononic crystals via topology optimization

Designing phononic crystals (PnCs) to exhibit the widest attainable band gap, or to encompass a designated frequency range, is imperative for the realization of PnCs tailored to specific functionalities. However, achieving a wide band gap of acoustic waves centered around a specified frequency remains a significant challenge. The challenge arises from the fact that the optimization objective function may be enhanced by disconnected air regions. Nonetheless, these disconnected regions obstruct the circulation of air, rendering PnCs devoid of practical engineering utility. In this study, we propose a topology optimization methodology aimed at crafting air/solid PnCs that maximize the band gap of acoustic waves at a specified central frequency. To ensure adequate air permeability within the PnCs, the optimization model incorporates the virtual temperature technique in conjunction with the minimum length scale technique. The topology of the PnC unit cell is represented with a low number of design variables through the material-field series expansion, and the Kriging-based optimization algorithm is used to solve the complicated optimization problem. Diverse optimized configurations are presented, and experimental findings compellingly underscore the efficacy of the proposed optimization approach in the design of phononic band gap crystals geared towards acoustic waves at the specified central frequency.

Distribution-Driven Optimization for Heat Pump and EV Hosting Capacity in LV Networks

The decarburization of heat and transport using electric vehicles (EVs) and heat pumps (HPs) needs an important investment in low-voltage (LV) networks. This article proposes an optimization approach to estimate the availability of headroom for domestic EV charging and optimization on LV networks under various penetrations of heat pumps. The proposed optimization approach is called dynamic differential annealed optimization (DDAO). The objective of the proposed approach is to maximize the hosting capability of low-voltage networks for EVs and HPs by optimizing the flexibility of EV charging. The DDAO is used to optimize different aspects of EV charging and LV networks. The DDAO approach is implemented in three steps: (a) HP headroom assessment, (b) EV optimization, and (c) validation. The performance of the proposed method is validated on a MATLAB platform and compared with the existing Binary Spring Search Algorithm (BSSA), Manta Ray Foraging Optimization Algorithm (MRFO), and Chaos Game Optimization methods. In comparison to the total prices of 3.5£ in BSSA, 4.5£ in MRFO, and 5.5£ in CGO, the proposed DDAO method achieves a lower total price of 3.2£. This suggests that the DDAO method is more cost-effective than the existing techniques.

Multi-objective optimization for designing porous scaffolds with controllable mechanics and permeability: A case study on triply periodic minimal surface scaffolds

Design of tissue scaffolds in terms of multiple objectives such as mechanical and biological performances simultaneously remains challenging. In this paper, we present the development of a topology optimization approach that enables to design porous scaffolds with consideration of both mechanical and fluid properties simultaneously. The relationship between structural parameters, mechanical and mass transport properties was investigated, and then the scaffolds were optimized under the constraint of given pore size, porosity and specific surface area. Case studies on the optimized design of triply periodic minimal surface (TPMS) scaffolds were performed to illustrate the effective of our approach. Simulation and experimental results demonstrated that the proposed optimization approach fully decouples the mechanical and mass transport properties, specifically, under given porosities, the elastic modulus and permeability of optimized scaffolds ranged from 1.33 GPa to 16.7 GPa and 0.07 × 10−8 m2 to 1.62 × 10−8 m2, respectively. Three types of optimized gradient scaffolds were designed and they all exhibited significant functional gradients and high adjustability. The proposed approach decouples synergy and enables individual customization of multi-physics properties, providing practical guidance for gradient scaffolds with desired performance.

Multi-objective particle swarm optimization for optimal scheduling of household microgrids

Addressing the challenge of household loads and the concentrated power consumption of electric vehicles during periods of low electricity prices is critical to mitigate impacts on the utility grid. In this study, we propose a multi-objective particle swarm algorithm-based optimal scheduling method for household microgrids. A household microgrid optimization model is formulated, taking into account time-sharing tariffs and users’ travel patterns with electric vehicles. The model focuses on optimizing daily household electricity costs and minimizing grid-side energy supply variances. Specifically, the mathematical model incorporates the actual input and output power of each distributed energy source within the microgrid as optimization variables. Furthermore, it integrates an analysis of capacity variations for energy storage batteries and electric vehicle batteries. Through arithmetic simulation within the Pareto optimal solution set, the model identifies the optimal solution that effectively mitigates fluctuations in energy input and output on the utility side. Simulation results confirm the effectiveness of this strategy in reducing daily household electricity costs. The proposed optimization approach not only improves the overall quality of electricity consumption but also demonstrates its economic and practical feasibility, highlighting its potential for broader application and impact.

Prediction of Optical Properties in Particulate Media Using Double Optimization of Dependent Scattering and Particle Distribution.

The prediction of optical properties dominated by light scattering in particulate media composed of high-concentration and polydisperse particles is greatly important in various optical applications. However, the accuracy and efficiency of light propagation simulations are still limited by the huge computational burden and complex interactions between dense and polydisperse particles. Here, we proposed a new optimization strategy that can effectively and accurately predict optical properties based on Monte Carlo simulation with particle size and dependent scattering corrections. Both the scattering parameters of particles and the experimental reflectance spectrum are fully examined for verification. Furthermore, using the weighted solar reflectance of particulate media as a representative optical property, both numerical simulations and experiments confirm the superiority and universality of the proposed optimization approach in a variety of materials systems. Moreover, our work can guide the design of particulate media with specific optical features insightfully and will be applicable in many fields involving multiparticle scattering.

Energy storage device sizing and energy management in building‐applied photovoltaic systems considering battery degradation

AbstractThis study aims to develop an optimization strategy for determining the optimal type and capacity of batteries in a building‐applied photovoltaic system, taking into account battery degradation, consumption profiles, and regional solar irradiation. Key performance indicators such as peak shaving, savings, net present value, self‐consumption, return on investment, and payback period are examined. The best trade‐off among these indicators is determined using the fuzzy decision‐making method. A study was conducted using real data from Kpenergy Company, focusing on a building with a 50 kW photovoltaic system located in Stockholm. Three cases were examined in MATLAB software, each categorized based on the type of contract between the utility (Vattenfall Company) and the subscriber. The results of these case studies highlight the effectiveness of the proposed optimization approach. Using the proposed approach, optimal batteries are determined, minimizing subscriber costs while maximizing profit.

Characterizing and predicting bluff-body solid fuel ramjet performances via shape design and multi-objective optimization model

In this work, we propose a rapid optimization approach to examine its application potential for the design and performance prediction and optimization of a solid fuel ramjet (SFRJ) with a bluff body. For this, the shape of the bluff body is parameterized first using the non-uniform rational B-spline method. We then develop a model for predicting SFRJ performances by incorporating both levy motion-gradient descent and support vector regression methods. It is found that a faster prediction is achievable, while the average error is maintained to be less than 5%. We then develop a multi-objective optimization model by considering the full thrust and minimum total pressure loss (TPL). The optimization model is examined using the non-dominated sorting genetic algorithm. A cost parameter is also created to facilitate the tradeoffs between the thrust and TPL in the Pareto front, when different bluff-body design configurations are considered. The present results reveal that an increase in the cost parameter will elevate the turbulence intensity within the SFRJs while drawing the incoming air closer to the fuel surface, resulting in an increase in thrust and regression rate, but the TPL will also increase. When prioritizing the TPL reduction in the design stage, the optimized solution reduces TPL by 50%. Meanwhile, the net thrust is shown to be decreased by less than 3.5%. Furthermore, flow-field investigation reveals that the improved performance of the optimized SFRJ is due to more uniform flow velocity gradients around the bluff body and a reduced rear vortex, resulting in reduced momentum loss. Our proposed optimization approach's robustness has been further confirmed with consistent performances, as the ramjet inlet speed varies over a broad range. It shows that our approach has great potential to be applied for the SFRJ performance prediction and optimization, being operated under various conditions.

Seismic Performance Assessment of a Cable-Damper Stopper Equipped for Coal-Fired Thermal Power Plants: Theoretical and Numerical Analyses

ABSTRACT A new cable-damper stopper equipped for Coal-Fired Thermal Power Plants (CFTPPs) was proposed in our previous paper, where a series of shaking table tests were carried out to investigate its seismic performance, so this paper aims to reveal the working mechanism of the stopper from the theoretical and numerical simulation aspects. The stopper is composed of three main components, i.e. a steel cable, a steel block and a viscous damper. First, the nonlinear restraint stiffness of the steel cable to the boiler was derived; second, the critical damping of the stopper, which makes the vibration of the boiler decay exponentially with no oscillation, was derived; third, an iterative method was proposed to globally tune the CFTPP structure, which makes it possible to obtain the optimal parameters of the cable-damper stopper, including the diameter of the steel cable and damping coefficient of the damper. Finally, finite element models were built and time history analyses were conducted to verify the proposed optimization approach. Firstly, the finite element models were proved valid through the comparison between the results of the simulations and the experiments. Then, based on the results of the simulations, the theoretical procedure was validated. The results revealed that the proposed cable-damper stopper reduced the seismic responses of displacements of the top floor and the boiler significantly, about 30.18% and 60.01% respectively compared with their counterparts of CFTPPs without stoppers.

Energy scheduling in a smart energy hub system with hydrogen storage systems and electrical demand management

The energy price uncertainty in energy markets is the main challenge for achieving economic modeling of energy consumption and generation. This paper proposes optimal energy scheduling in a smart energy hub (SEH) under the uncertainty of electricity prices in the day-ahead. The proposed optimization approach is implemented by a two-layer interval approach for minimizing energy generation cost. In first layer optimization, demand side management (DSM) for electrical demand subject to the optimal level of electrical consumption is implemented. In second-layer optimization, the interval approach is used for modeling uncertainty. The energy generation cost is converted to bi-objective functions including average and deviation costs with conflicting nature than each other. Also, the multi-performance of the hydrogen storage system based on electrical and natural gas generation is considered for managing uncertainties in second layer optimization. Since the interval optimization approach is proposed in the second layer, minimizing the average cost and deviation cost are formulated as bi-objective functions by the augmented epsilon-constraint method. In the following, the LINMAP procedure is utilized to achieve maximum compatibility and the best trade-off consequence in the Pareto frontier of the multi-objective functions. Finally, the proposed optimization approach is modeled as a numerical simulation in the case studies to verify obtained results. The participation of the DSM and the hydrogen storage systems leads to minimizing the average and deviation costs by 3.03 % and 3.16 % in comparison with non-participation.

Topology optimization of differentiable microstructures

Recent years have seen a growing interest in topology optimization of functionally graded microstructures, characterized by an array of microstructures with varying volume fractions. However, microstructures optimized at slightly different volume fractions do not necessarily connect well when placed adjacently. Furthermore, optimization is commonly performed on a finite set of volume fractions, limiting the number of microstructure configurations.In this paper, we introduce the concept of differentiable microstructures, which are parameterized microstructures that exhibit continuous variations in both geometry and mechanical properties. To construct such microstructures, we propose a novel formulation for topology optimization. In this approach, a series of 2-dimensional microstructures is represented using a height field, and the objective is to maximize the bulk modulus of the entire series. Through this optimization process, an initial microstructure with a small volume fraction undergoes non-uniform transformations, generating a series of microstructures with progressively increasing volume fractions. Notably, when compared to traditional uniform morphing methods, our proposed optimization approach yields a series of microstructures with bulk moduli that closely approach the theoretical limit.

Improving Directional Overcurrent Relay Coordination in Distribution Networks for Optimal Operation Using Hybrid Genetic Algorithm with Sequential Quadratic Programming

In recent years, with the growing popularity of smart microgrids in distribution networks, the effective coordination of directional overcurrent relays (DOCRs) has presented a significant challenge for power system operators due to the intricate and nonlinear nature of their optimization model. Hence, this study proposes a hybrid GA-SQP algorithm to enhance the coordination of directional overcurrent relays (DOCRs) in radial and non-radial interconnected distributed power networks. The proposed approach combines the advantages of both the genetic algorithm (GA) and sequential quadratic programming (SQP) methods to optimize the objective function of relay coordination in the best manner. Thus, the proposed hybrid techniques improved the convergence of the problem and increased the likelihood of obtaining a globally optimal solution. Finally, to validate the effectiveness of the proposed algorithm, it was tested through three case studies involving the IEEE 3-Bus, 8-Bus, and modified 30-Bus distribution networks. In addition, the results were compared to those obtained using previous methods. The results obtained from the comparison of the proposed method and recent advanced research indicate that the proposed optimization approach is preeminent in terms of accuracy and total operating time as well as the continuity of the minimum margin time requirements between the primary/backup relay pairs.

Optimization Strategy for Generative Adversarial Networks Design

Generative Adversarial Networks (GANs) are a powerful class of deep learning models that can generate realistic synthetic data. However, designing and optimizing GANs can be a difficult task due to various technical challenges. The article provides a comprehensive analysis of solution methods for GAN performance optimization. The research covers a range of GAN design components, including loss functions, activation functions, batch normalization, weight clipping, gradient penalty, stability problems, performance evaluation, mini-batch discrimination, and other aspects. The article reviews various techniques used to address these challenges and highlights the advancements in the field. The article offers an up-to-date overview of the state-of-the-art methods for structuring, designing, and optimizing GANs, which will be valuable for researchers and practitioners. The implementation of the optimization strategy for the design of standard and deep convolutional GANs (handwritten digits and fingerprints) developed by the authors is discussed in detail, the obtained results confirm the effectiveness of the proposed optimization approach.

Museum and Visitor Interaction and Feedback Orchestration Enabled by Labor Economics

In this article, we address the problem of modeling and orchestrating the interactions between a museum and its visitors, viewing the system as a cyber–physical–social system (CPSS). In particular, the museum operator provides monetary rewards to the visitors in exchange for their contributions, which are expressed as their total number of provided feedback evaluations of visited exhibits over their touring time. The interactions among the museum operator and visitors are captured in appropriately designed utility functions following the principles of labor economics, while the visitors’ behavioral characteristics are utilized to define their unique types. Under such a setting and formulation, the goal of the museum operator is to optimize their profit and benefits while jointly satisfying the visitors’ quality of experience prerequisites as reflected via their utility functions. The corresponding optimization problem is treated and solved under the general and realistic cases of incomplete information, wherein the museum operator estimates the visitors’ types probabilistically. The resulting outcome, referred to as the “optimal contract”, jointly determines the visitors’ optimal contributions, as well as the museum operator’s optimal amount of personalized rewards provided to each visitor. The performance of the proposed approach is evaluated through modeling and simulation, and detailed numerical results are presented to demonstrate the key benefits of the proposed optimization approach versus either type-agnostic or heuristic alternatives.

Optimized Packing of 3D Objects Bounded by Spherical and Conical Surfaces

Introduction. Optimizing the packing of arbitrary geometric objects in additive manufacturing opens up new possibilities for increasing the efficiency of additive manufacturing of parts of a complex configuration due to the saving of energy, material and time resources. Additive manufacturing, a cornerstone in fields such as space engineering, medicine, mechanical engineering, and energy, has its efficiency hinging on the optimization of the 3D printing process. Given its widespread application, refining this process is of utmost importance. The purpose of the paper. The paper aims to develop an approach for packing assembled parts of complex geometry in the working area of a 3D printer, while adhering to the standards of 3D printing. Results. For the analytical description of the complex shaped industrial products, a, so called, “composed spherical cone” is used. This generates a family of such objects as spheres, cylinders, spherical cylinders, cones, truncated cones and spherical discs. Using the normalized quasi-phi-function of composed spherical cones, a mathematical model of the problem is presented in the form of a nonlinear programming problem. A solution strategy is developed, encompassing three primary stages: generation of feasible starting points, search of local minima and search of a better local minimum. Numerical examples of packing various industrial products in a 3D printer chamber is provided. 3D-parts are approximated by composed spherical cones with different metric parameters. Conclusions. The conducted numerical simulation confirms the effectiveness of the proposed optimization approach. This study emphasizes the importance of further research and innovation in the field of 3D printing and its optimization, and also demonstrates the potential of using mathematical models to solve practical problems in a production environment. Keywords: packing, assembled spherical cone, mathematical modeling, optimization, additive manufacturing.

Power system resilience against climatic faults: An optimized self-healing approach using conservative voltage reduction

The resilience of power systems is critical in mitigating faults caused by the impending effects of climate change. Typical operational methods, such as network reconfiguration, may be insufficient to mitigate faults in the event of a contingency. On the other hand, reducing system demand may help increase the number of restored loads. As a result, this paper proposes a novel self-healing optimization approach based on a power grid concept known as conservative voltage reduction (CVR), which results in system demand reduction. The proposed optimization model is formulated as a mixed integer non-linear (MINLP) problem to fulfill an objective of minimizing unserved loads within the system. Voltage, thermal capacity, system radiality, and several more constraints have been taken into consideration while formulating the proposed model to handle both grid-connected and isolated modes of operation. Both dispatchable and non-dispatchable distributed generators (DGs) are taken into consideration. Dispatchable DGs are assumed to be capable of switching back and forth between constant power (PQ) and droop controls for the purpose of grid following and grid forming in grid-connected and isolated modes of operation, respectively. The proposed optimization approach is coded and solved using the General Algebraic Modeling System (GAMS) software. The IEEE 69-bus power distribution test system is utilized to test the validity and superiority of the proposed model. The results show that the proposed model effectively increases the system resilience by reducing the unserved power/energy within the system following a contingency.

Fuzzy comprehensive evaluation model in the evaluation of English teaching quality

Evaluating English teaching quality is vital for improving knowledge-based developments through communication for different aged students. Teaching quality assessment relies on the teachers’ and students’ features for constructive progression. With the development of computational intelligence, optimization and machine learning techniques are widely adapted for teaching quality assessment. In this article, a Quality-centric Assessment Model aided by Fuzzy Optimization (QAM-FO) is designed. This optimization approach validates the student-teacher features for a balanced model assessment. The distinguishable features for improving students’ oral and verbal communication from different teaching levels (basic, intermediate, and proficient) are extracted. The extracted features are the crisp input for the fuzzy optimization such that the recurring fuzzification detains the least fit feature. Such features are replaced by the level-based teaching and performance feature that differs from the previous fuzzy input. This replacement is pursued until a maximum recommendable feature (performance/ learning) is identified. The identified feature is applicable for different teaching levels for improving the quality assessment. Therefore, the proposed optimization approach provides different feasible recommendations for teaching improvements.

Performance-Aware NILM Model Optimization for Edge Deployment

Non-Intrusive Load Monitoring (NILM) describes the extraction of the individual consumption pattern of a domestic appliance from the aggregated household consumption. Nowadays, the NILM research focus is shifted towards practical NILM applications, such as edge deployment, to accelerate the transition towards a greener energy future. NILM applications at the edge eliminate privacy concerns and data transmission-related problems. However, edge resource restrictions pose additional challenges to NILM. NILM approaches are usually not designed to run on edge devices with limited computational capacity, and therefore model optimization is required for better resource management. Recent works have started investigating NILM model optimization, but they utilize compression approaches arbitrarily without considering the trade-off between model performance and computational cost. In this work, we present a NILM model optimization framework for edge deployment. The proposed edge optimization engine optimizes a NILM model for edge deployment depending on the edge device’s limitations and includes a novel performance-aware algorithm to reduce the model’s computational complexity. We validate our methodology on three edge application scenarios for four domestic appliances and four model architectures. Experimental results demonstrate that the proposed optimization approach can lead up to a 36.3% average reduction of model computational complexity and a 75% reduction of storage requirements.