Objective Optimization Research Articles

Optimization of Personalized Mass Customization Building Portfolio by Integrating BIM and NSGA Algorithms

The objective of this study is to optimize the personalized mass customization building portfolio by using BIM and NSGA algorithms to achieve the desired balance between cost and efficiency. The study uses BIM software for cost simulation, and NSGA-II algorithm for multi-objective optimization. The mathematical model of building cost and duration is constructed and the multi-objective optimization algorithm is applied to achieve optimization under multiple objectives. The results of the study show that after 20,000 generations of iterations, 191 Pareto solutions are determined to be valid, and the stability and uniform distribution of the optimization objectives are verified, with the percentage of valid solutions reaching 92\%. In the conducted VIKOR scenario analysis, three different cost-effective optimization schemes are proposed depending on user preferences. The method of integrating BIM and NSGA algorithm can effectively improve the cost-effectiveness of building design, and provides a feasible and efficient optimization path for personalized mass-customized buildings.

A novel data center free radiating system driven by waste heat and wind energy system (DCFRWWs) and its operation performance analysis

Refrigeration equipment for data centers consumes a significant portion of global electricity, resulting in high Power Usage Effectiveness (PUE) values and increased entropy of the data center. To address these challenges, this study proposes a novel data center free radiating system driven by waste heat and wind energy (DCFRWWs). This approach recovers low-grade heat energy from the hot air heated by the central processing units (CPUs) and transform it into work. Unlike conventional cooling systems, the DCFRWWs do not require additional energy to meet CPU cooling demands and can effectively suppress the entropy increase of the data center. The Bernoulli equation was used to establish the mathematical model for DCFRWWs, which explained the underlying mechanisms of pipeline wind speed and the influence of ambient wind speed and pipeline inlet temperature on system operating characteristics. In addition, this study employed pipeline height and diameter as decision variables, while maximizing exergy efficiency served as the optimization objective. To obtain the optimal pipeline size (height and diameter) that is effective under real-world experimental conditions, the optimization calculations were conducted using weather data from Qinhuangdao. To verify the reliability and accuracy of the model, an experimental system was constructed, and the simulation results indicated that the relative error rates for the three key parameters are as follows: pipeline wind speed (33.09%), pipeline inlet temperature (0.59%), and CPU temperature (3.85%). A single factor analysis was performed on 14 different operating conditions to analyze the operating characteristics of the DCFRWWs. Moreover, the applicability verification conducted in three distinct cities across different regions suggest that the system is indeed suitable for diverse settings. The DCFRWW system, utilizing the dual forces of the chimney effect of waste hot air in pipelines and the extraction force generated by unpowered roof ventilator, achieves the objective of free heat dissipation. Furthermore, the DCFRWWs can not only be applied in the field of data center cooling but also serves as a thermodynamic reference for low-grade waste heat recovery in other sectors, thereby further expanding the scope of direct utilization of wind energy.

The multi-objective optimization and mix parameter evaluation of one-part alkali-activated grouting material

To obtain a green and cost-effective one-part alkali-activated grouting material (OAGM) with good comprehensive performance for subgrade reinforcement and determine its proper mix parameters. In this study, using response surface methodology (RSM), multiple objective particle swarm optimization (MOPSO) algorithm and the technique for order preference by similarity to an ideal solution (TOPSIS) algorithm coupled with entropy weight method, two multi-objective optimization schemes for OAGM were performed, one only considering performance and another considering performance, embodied CO2 emission (ECO2e) and cost. Then, the correlation-based feature selection (CFS) algorithm was used to evaluate the importance of various molar ratios as mix parameters on the performance of OAGM. Finally, the effects of key mix parameters on the microstructure and reaction products of OAGM were studied. RSM results show that W/S (water to solid) ratio, borax (BR) content and UCG content chiefly govern the flowability, initial setting time and mechanical strength of OAGM respectively, and the interactions between some factors also significantly affect these properties. MOPSO and entropy-TOPSIS algorithm can be used to optimize OAGM objectively. Two optimal OAGM pastes both have better comprehensive performance and much lower ECO2e than ordinary Portland cement (OPC) paste. And the optimal OAGM considering performance, ECO2e and cost even has 7.68% lower cost than OPC paste. But another optimal OAGM has the best comprehensive performance. CFS results show that the molar ratios of Si/Al, Al/Ca, Na2O/H2O, B2O3/CaO and B2O3/Al2O3 are the key mix parameters of OAGM. Al/Ca rather than Si/Al is the most important mix parameter to the 28-day compressive strength. And higher Si/Al and Al/Ca deteriorates the microstructure and hinders the formation of C-A-S-H gel, while higher Na2O/H2O has the opposite effect. This study provides guidance for objective optimization and the determination of proper mix parameters of OAGM, and promotes the market-oriented application of OAGM.

Multi-dimensional resource allocation strategy for LEO satellite communication uplinks based on deep reinforcement learning

In the LEO satellite communication system, the resource utilization rate is very low due to the constrained resources on satellites and the non-uniform distribution of traffics. In addition, the rapid movement of LEO satellites leads to complicated and changeable networks, which makes it difficult for traditional resource allocation strategies to improve the resource utilization rate. To solve the above problem, this paper proposes a resource allocation strategy based on deep reinforcement learning. The strategy takes the weighted sum of spectral efficiency, energy efficiency and blocking rate as the optimization objective, and constructs a joint power and channel allocation model. The strategy allocates channels and power according to the number of channels, the number of users and the type of business. In the reward decision mechanism, the maximum reward is obtained by maximizing the increment of the optimization target. However, during the optimization process, the decision always focuses on the optimal allocation for current users, and ignores QoS for new users. To avoid the situation, current service beams are integrated with high- traffic beams, and states of beams are refactored to maximize long-term benefits to improve system performance.Simulation experiments show that in scenarios with a high number of users, the proposed resource allocation strategy reduces the blocking rate by at least 5% compared to reinforcement learning methods, effectively enhancing resource utilization.

ResUNet involved generative adversarial network-based topology optimization for design of 2D microstructure with extreme material properties

Topology optimization is one of the most common methods for design of material distribution in mechanical metamaterials, but resulting in expensive computational cost due to iterative simulation of finite element method. In this work, a novel deep learning-based topology optimization method is proposed to design mechanical microstructure efficiently for metamaterials with extreme material properties, such as maximum bulk modulus, maximum shear modulus, or negative Poisson’s ratio. Large numbers of microstructures with various configurations are first simulated by modified solid isotropic material with penalization (SIMP), to construct the microstructure data set. Subsequently, the ResUNet involved generative and adversarial network (ResUNet-GAN) is developed for high-dimensional mapping between optimization parameters and corresponding microstructures to improve the design accuracy of ResUNet. By given optimization parameters, the well-trained ResUNet-GAN is successfully applied to the microstructure design of metamaterials with different optimization objectives under proper configurations. According to the simulation results, the proposed ResUNet-GAN-based topology optimization not only significantly reduces the computational duration for the optimization process, but also improves the structure precise and mechanical performance.

Optimization analysis of a Stokes polarimeter forbroadband liquid crystal variable retarders underthe optimal objective function.

A liquid crystal variable retarder (LCVR) is the core device to realize fast and high-precision broadband polarization imaging, and its ability to suppress the noise will have an impact on the polarization measurement results. In order to obtain better imaging quality and measurement accuracy, it is crucial to solve the optimization problem of the LCVR. In this paper, the optimal objective function for solving the optimization problem of the LCVR is analytically derived and verified based on the genetic algorithm in the band range of 350-700nm. Meanwhile, considering that the minimum number of four measurements at this time cannot achieve the optimal state, the relationship between the number of measurements and the overall performance relative to the error propagation (optimized conditions number) is discussed. The results show that a better optimal set of angles can be obtained by using the optimal objective function. In this paper, a set of the most favorable angles is obtained, and the optimized average of the CN is 2.0000, which is reduced by 0.32% compared with previous optimization results and is closer to the ideal value of the CN. In addition, in this paper, the noise immunity of the set of most favorable angles is simulated and analyzed, and the optimized system can effectively improve the measured performance of the wide-band liquid crystal variable retarder polarimeter.

Multi-agent system consistency-based cooperative scheduling strategy of regional integrated energy system

This article presents a multi-agent system (MAS)-consistency-based cooperative scheduling strategy for regional integrated energy system to address the real-time economic scheduling problem with high penetration of multiple flexible loads. First, the MAS-based hierarchical optimal operation framework is developed to perform the real-time scheduling, where the framework with distributed control is further developed into the hierarchical framework with low-dimensional adjacency matrix. Furthermore, the economic scheduling optimization models corresponding to hierarchical distributed control mode and distributed control mode are established. The optimal objective of the proposed control structure is to 1) minimize the total operation cost, 2) optimize the electrical and heat power of all dispatchable units in balancing energy supply-demand. The operational rule of following heat load for hierarchical distributed control mode and a hill-climbing search model for distributed control mode are proposed to achieve the consistency of agents, so as to realize the cooperative optimal dispatch of electricity and heat vectors. Simulation results validate the effectiveness of the proposed MAS-based strategy in optimizing the cooperative operation of electricity-heat vectors. The real-time energy generation and load demand can be accurately balanced. The proposed strategy can perform efficient real-time economic scheduling and has satisfied adaptive capability for random variations of flexible loads.

Design and experiment of Panax notoginseng root orientation transplanting device based on YOLOv5s.

Consistent root orientation is one of the important requirements of Panax notoginseng transplanting agronomy. In this paper, a Panax notoginseng orientation transplanting method based on machine vision technology and negative pressure adsorption principle was proposed. With the cut-main root of Panax notoginseng roots as the detection object, the YOLOv5s was used to establish a root feature detection model. A Panax notoginseng root orientation transplanting device was designed. The orientation control system identifies the root posture according to the detection results and controls the orientation actuator to adjust the root posture. The detection results show that the precision rate of the model was 94.2%, the recall rate was 92.0%, and the average detection precision was 94.9%. The Box-Behnken experiments were performed to investigate the effects of suction plate rotation speed, servo rotation speed and the angle between the camera and the orientation actuator(ACOA) on the orientation qualification rate and root drop rate. Response surface method and objective optimisation algorithm were used to analyse the experimental results. The optimal working parameters were suction plate rotation speed of 5.73 r/min, servo rotation speed of 0.86 r/s and ACOA of 35°. Under this condition, the orientation qualification rate and root drop rate of the actual experiment were 89.87% and 6.57%, respectively, which met the requirements of orientation transplanting for Panax notoginseng roots. The research method of this paper is helpful to solve the problem of orientation transplanting of other root crops.

Design and Experiment of a High-Clearance Mid-Tillage Weeder for Dryland Farming

Mechanical weeding is an important technical means for organic and regenerative agricultural systems. Current weed control equipment has a variety of problems, such as difficulty adapting to high-stalk crops and poor operational quality. A high-clearance mid-tillage weeder (HMTW) has been developed to meet the mechanical weed control needs of high-stalk crops. The weeder mainly comprises a suspension device, a frame, parallel four-rod profiling mechanisms, weeding operation components, and depth-limiting soil-cutting devices. Based on the agronomic requirements of dryland flat planting, the overall structure of the HMTW was determined, and the weeding unit and flat shovel hoe were designed. Theoretical analysis was conducted on the depth stability of the HMTW, and an optimization mathematical model of the HMTW was established to further improve its tillage depth stability for agronomic requirements. The optimization objective was to minimize the deflection angle (∆β) of the profiling rod on a vertical plane, and the parameters of the parallel four-rod profiling mechanism were optimized. Based on the optimized structural parameters, a prototype of the HMTW was developed and evaluated. The test results show that the optimized HMTW exhibited a good weeding effect, and the tillage depth stability was within the design operating range. When the driving speed was 1.0 m/s and the tillage depth was 8 cm, the weed removal rate, seedling injury rate, seedling burial rate, and qualified rate of tillage depth were 90.8%, 3.2%, 4.1%, and 94%, respectively. The proposed HMTW successfully meets the weeding agronomic requirements of high-stalk crops for dryland farming, and the performance analysis and optimization models provide technical references for the design and development of such structures.

An analytical form of ring artifact correction for computed tomography based on directional gradient domain optimization.

Ring artifact is a common problem in Computed Tomography (CT), which can lead to inaccurate diagnoses and treatment plans. It can be caused by various factors such as detector imperfections, anti-scatter grids, or other nonuniform filters placed in the x-ray beam. Physics-based corrections for these x-ray source and detector non-uniformity, in general cannot completely get rid of the ring artifacts. Therefore, there is a need for a robust method that can effectively remove ring artifacts in the image domain while preservingdetails. This study aims to develop an effective method for removing ring artifacts from reconstructed CTimages. The proposed method starts by converting the reconstructed CT image containing ring artifacts into polar coordinates, thereby transforming these artifacts into stripes. Relative Total Variation is used to extract the image's overall structural information. For the efficient restoration of intricate details, we introduce Directional Gradient Domain Optimization (DGDO) and design objective functions that make use of both the image's gradient and its overall structure. Subsequently, we present an efficient analytical algorithm to minimize these objective functions. The image obtained through DGDO is then transformed back into Cartesian coordinates, finalizing the ring artifact correctionprocess. Through a series of synthetic and real-world experiments, we have effectively demonstrated the prowess of our proposed method in the correction of ring artifacts while preserving intricate details in reconstructed CT images. In a direct comparison, our method has exhibited superior visual quality compared to several previous approaches. These results underscore the remarkable potential of our approach for enhancing the overall quality and clinical utility of CTimaging. The proposed method offers an analytical solution for removing ring artifacts from CT images while preserving details. As ring artifacts are a common problem in CT imaging, this method has high practical value in the medical field. The proposed method can improve image quality and reduce the difficulty of disease diagnosis, thereby contributing to better patientcare.

Empty container repositioning problem using a reinforcement learning framework with multi-weight adaptive reward function

ABSTRACT In logistics networks, empty container congestion and scarcity often stem from trade imbalance and supply-demand mismatch. This paper focuses on the problem of empty container repositioning in maritime logistics and proposes a reinforcement learning framework that integrates a self-adaptive mechanism for adjusting the weights of a multi-objective reward function. The objective is to enhance container utilization and reduce scheduling costs. By reviewing the development of the empty container repositioning problem and analysing the advantages of using reinforcement learning to address the temporal and spatial complexity, the problem is modelled as a Markov decision process and tackled using reinforcement learning techniques. To achieve the optimization objectives, which involve reducing resource shortages at various locations and minimizing resource repositioning costs, a multi-objective reward function is introduced to capture the mutually constrained preferences. The weights of the reward function are dynamically adjusted to account for the potential time-varying preferences of the agent, mitigating the issue of poor generalization performance associated with fixed-weight reward functions. Comparative experimental analysis against conventional reinforcement learning algorithms demonstrates the superior performance of the proposed approach in problem solving. Based on the results and practical requirements of the case study, relevant recommendations for addressing empty container repositioning are presented.

Two-Stage Optimal Scheduling Based on the Meteorological Prediction of a Wind–Solar-Energy Storage System with Demand Response

With large-scale wind and solar power connected to the power grid, the randomness and volatility of its output have an increasingly serious adverse impact on power grid dispatching. Aiming at the system peak shaving problem caused by regional large-scale wind power photovoltaic grid connection, a new two-stage optimal scheduling model of wind solar energy storage system considering demand response is proposed. There is a need to comprehensively consider the power generation cost of various types of power sources, day-ahead load forecasting information, and other factors and plan the day-ahead output plan of the energy storage system with the minimum system operation cost as the optimization objective of day-ahead dispatching. The demand response strategy is introduced into the time-ahead optimal scheduling, and the optimization of the output value of the energy storage system in each period is studied with the goal of minimizing the system adjustment cost. The particle swarm optimization algorithm is used to solve the model, and the IEEE33 node system is used for an example simulation. The results show that using the demand response and the collaborative effect of the energy storage system can suppress the uncertainty of wind power and photovoltaic power, improve the utilization rate of the system, reduce the power generation cost of the system, and achieve significant comprehensive benefits.

Integrated Demand Response for Micro-Energy Grid Accounting for Dispatchable Loads

Micro-energy networks are the smallest element of integrated energy systems, and tapping into the integrated demand response potential of micro-energy networks is conducive to improving energy use efficiency and promoting the development of new energy sources on a large scale. This paper proposes a day-ahead integrated demand response strategy for micro-energy grid that takes into account the dispatchable loads. Considering the gradient use of thermal energy, a typical micro-energy grid structure including electricity, gas, medium-grade heat, low-grade heat, and cold energy is constructed, a comprehensive energy equipment model is established, and the refined scheduling models of the dispatchable loads are given. On this basis, with the operating economy of the micro-energy grid as the optimization objective, the integrated demand response strategies of tariff-type and incentive-type are proposed. Through case study analysis, it is verified that the proposed strategy can optimize the energy consumption structure of the micro-energy grid under the guidance of time-of-use tariffs, reducing the operating costs. The proposed strategy fully exploits the demand response potential of the micro-energy grid through the dispatchable loads and the multi-energy complementarity of electricity, heat, and cold, realizes the comprehensive coordination and optimization of source-network-load-storage, provides a larger peak-regulating capacity, and exhibits practical applicability in engineering.

Design of locally arranged sensor arrays in wearable OPM-MEG based on sensor volume constraints

Optically pumped magnetometers-based magnetoencephalography (OPM-MEG) has a more flexible sensor configuration. This implies that OPM-MEG can be customized as locally arranged sensor arrays (LASAs) when the number of sensors is limited. Currently, various methods are proposed to optimize LASA for accurately estimating source currents. However, the sensor volume, as a practical constraint in the construction of an OPM-MEG system, was not taken into account during the design of sensor arrays. In this study, we proposed a sensor-array design method for LASAs that considers the physical size of OPM sensors, with the array sensitivity metrics serving as the optimization objective. To illustrate the effectiveness of the proposed method, several LASAs covering typical brain functional regions were designed and tested in both simulations and practical OPM-MEG experiments. The results indicated that the designed LASAs under sensor volume constraints demonstrated reduced source localization errors and focused localization range for the target source area.

Study on flexible job shop scheduling problem considering energy saving

The Flexible Job Shop Scheduling Problem (FJSP) is an extension of the classical Job Shop Scheduling Problem (JSP). The research objective of the traditional FJSP mainly considers the completion time, but ignores the energy consumption of the manufacturing system. In this paper, a mathematical model of the energy-efficient flexible job shop scheduling problem is constructed. The optimization objectives are completion time, delay time, and total equipment energy consumption. To solve the model, an improved non-dominated sorting genetic algorithm (CT-NSGA-II) is proposed to obtain the optimal scheduling solution. First, the heuristic rules of GLR were used to generate the initial population with good quality and diversity. Second, different crossover and variation operators are designed for the process sequencing and equipment selection parts to enhance the diversity of the evolutionary population. The sparsity theory is introduced to find sparse solutions and three neighborhood structures are designed to perform local search on sparse solutions to improve the uniformity of the optimal solution set distribution. Finally, a competitive selection strategy based on the bidding mechanism is proposed for the Pareto optimal solution set to obtain a better scheduling scheme. The experimental results show that the proposed improved algorithm is feasible and effective in the FJSP problem considering energy consumption, and the algorithm has some application value in improving the efficiency of smart shop operation.

Experimental and numerical study on flow characteristics of dual‐cavity die based on multi‐objective optimization

AbstractSlot die coating is a common method of manufacturing electrodes for lithium‐ion batteries. In this paper, a multi‐objective optimization research on the geometry of dual‐cavity die is investigated based on the numerical simulation of lithium battery anode slurry flow combined with the surface response optimization method. First, we use the Plackett–Burman test method to screen the geometric parameters significantly affecting the optimization objective. Then, based on the Box–Behnken response surface analysis method, we determine the optimal combination of parameters. The results show that the inner cavity radius and slot gap are the main structural variables affecting exit‐velocity uniformity and die deformation. The slot gap and slot length mainly affect the inlet pressure. The coefficient of deviation of exit velocity uniformity and average inlet pressure of the optimized die structure decreased by 51.3% and 7.6%, respectively. This research provides theoretical guidance for the design of slot coating dies.Highlights Screening significant geometric parameters using Plackett–Burman test methodology. Box–Behnken response surface analysis to determine optimal parameters. Velocity uniformity and inlet pressure increased by 51.3% and 7.6%, respectively.

A bearing RUL prediction approach of vibration fault signal denoise modeling with Gate-CNN and Conv-transformer encoder

The operating conditions of rolling bearings are complex and variable, and their vibration monitoring signals are filled with strong noise interference, resulting in a low accuracy in remaining useful life (RUL) prediction. For this issue, this paper proposes a denoising method with vibration fault signals modeling, and a novel RUL prediction method with Gate-convolutional neural networks (CNN) and Conv-Transformer encoder. Firstly, the theoretical fault signal is obtained through the vibration fault signal model, and the quality of the extracted features is improved by the wavelet threshold denoising algorithm in the process of feature extraction and selection. Moreover, the CNN is combined with the gating mechanism to construct a feature extractor with the feature evaluation function, and the convolution layers are introduced into the transformer to expand the encoder’s ability to explore local information in temporal data. By using fixed-time step temporal features as the input to the prediction module and minimizing the Huber function as the optimization objective, the relationship between temporal features and RUL is obtained. The comparison with the existing state-of-the-art RUL methods illustrates that the combination of gate control and convolutional structure proposed in this paper can not only reduce the prediction error of the model but also improve its generalization ability and robustness.

Optimization of Flanged Diffuser for Small-Scale Wind Power Applications

The development of renewable and clean energy has become more crucial to societies due to the increasing energy demand and fast depletion of fossil fuels. A state-of-the-art design for an augmented wind turbine has been introduced in the past years to increase the efficiency of compact horizontal axis wind turbines, exceeding the ideal Betz’s limit of the maximum energy captured from the wind. The optimization of the flanged diffuser - so-called diffuser augmented wind turbine DAWT - is investigated numerically using the multi-objective genetic algorithm “MOGA”. A 2D computational model is developed using ICEM CFD and solved by ANSYS Fluent. The Turbulence model selected is shear stress transport K-omega, with a pressure-based solver and a coupled algorithm scheme. The optimization objectives are to maximize the velocity ratio at the shroud throat and minimize shroud form dimensions. 517 design points were solved, and the design dimensions were categorized into four types: compact, small, medium, and large design. The results showed that the diffuser dimensions are the main parameters to increase velocity inside the shroud throat, where a long diffuser with a low converging angle drags more air inside the shroud, reaching in some cases more than double the upwind velocity. While the nozzle and flange are also effective in the different design types. It was found that a super long diffuser with a length ratio of 2.9 LD to throat diameter D is optimal with a diverging angle of 7.6˚, accompanied by a nozzle of ratio 1.2 LN/D and 12.6˚ converging angle and a flange length ratio of 0.6 LF/D. This optimal design increased the velocity ratio by almost 2.5 times.

Grid-connected multi-microgrid system operational scheduling optimization: A hierarchical improved marine predators algorithm

This study deals with grid-connected multi-microgrid system (MMGS) operation scheduling optimization problem using improved marine predators algorithm. Studies are lacking a system operation strategy designed to involve the internal power interaction and load demand response in the scheduling manipulation is short. This study involves power interaction and demand response to achieve the MMGS operational economy and reliability. Each MGS is combined to main grid with the continuous use of renewable energy and MMGS rapid development. This study proposes the analytical steps as follows. (1) the MMGS construction model includes wind energy and photovoltaic; (2) the optimization objective function so as to minimize the cost is constructed according to the characteristics of the equipment in the system; (3) a hierarchical improved marine predators algorithm is assumed to boost the optimal effect; (4) the test of the enhanced algorithm is completed and the optimization problem of the total running cost is solved; and; (5) a system operation strategy is designed considering the internal power interaction and load demand response in the MMGS scheduling operation. A different algorithms comparative analysis is performed. The proposed scheduling optimization approach is testified among analysis of specific examples, and the total operating cost is reduced by 11.19%.

Multi objective optimization scheduling of unmanned warehouse handling robots based on A star algorithm

SummaryThe sorting efficiency of the warehouse management and handling robots (automated guided vehicles [AGVs]) in an unmanned warehouse affects the delivery and lead time of orders. Studying the collaborative sorting of multiple AGVs can significantly improve work efficiency. The goal of this paper is to minimize the overall shortest transportation path and time while ensuring the completion of all orders and corresponding recycling or return tasks. This paper conducts experiments on global path planning for multiple AGVs based on the A* algorithm, both without considering collision and deadlock situations, and considering collision and deadlock situations. The results show that without considering collision and deadlock situations, the method of matching fixed storage locations and picking stations can improve overall efficiency, while considering collision and deadlock problems will result in reduced efficiency. In the future, the model will be further optimized taking into account actual conditions to improve overall efficiency.