Multi-objective Optimization Research Research Articles

Multi-objective optimization research on nonlinear energy sink system of finite-length beam on elastic medium

Multi-objective optimization research on nonlinear energy sink system of finite-length beam on elastic medium

Multi-objective optimization and scheduling research on electric bicycle charging and discharging in distribution networks

Abstract With the continuous development of urbanization, the rapid growth of electric bicycles has become a prominent trend. The number of battery-swapping stations for convenient battery replacement has also increased. The large-scale and unregulated connection of electric bicycles and battery-swapping stations to the power grid can significantly impact the distribution network, leading to localized overloads and affecting grid stability. Therefore, this paper proposes a multi-objective optimization scheduling strategy for electric bicycle charging and dis-charging based on both the transmission and distribution systems. Firstly, in the transmission system, the objective is to reduce the operating costs of generators and user charging costs. Sub-sequently, in the distribution system, the goal is to minimize network losses, considering system constraints and the spatial migration characteristics of electric bicycles. The study establishes a multi-objective optimization problem. Simulation analyses were conducted on the power system models based on a standard 10-machine transmission network and an IEEE 33-node distribution network to verify the effectiveness of the proposed multi-objective optimization scheduling strategy.

Multi-objective optimization research of open and closed air brayton cycle

Air Brayton cycle obtains the significant advantages of high efficiency, compact structure, easy medium requirement and so on, which is one of the most suitable choices for the mobile nuclear power conversion system. In this paper, system-component combined design method is used to establish the open and closed air Brayton cycle of diverse configurations. Based on performance and compactness targets, the multi-objective optimization is carried out to find the optimal design by nondominated sorting genetic algorithm II and entropy weight method. According to the analysis, the closed intercooling reheating recuperating cycle is the configuration with the best comprehensive performance, with cycle efficiency of 33.58 %, power density of 175.39 kW m−3 and power mass ratio of 68.60 kW t−1. The open simple recuperating cycle is the configuration with the smallest volume and mass, with cycle efficiency of 20.01 %, system volume of 6.56 m3 and system mass of 19.98 t. The closed intercooling recuperating cycle is the most balanced configuration, with cycle efficiency of 31.33 %, volume of 9.06 m3 and system mass of 24.97 t. Based on the optimal results, off-design performance analysis of different environmental conditions is also carried out for the configurations above.

Multi-objective cooperation optimization research on dynamic response and energy loss of high-speed solenoid valve for diesel engine injector

The dynamic response and operational reliability of high-speed solenoid valve (HSV) for diesel engine injector are the main indicators to measure their performance. At high-frequency, the eddy current energy and Joule energy generated by the HSV will be converted into heat, which has a significant impact on the service life of HSV. The optimization of HSV involves the interaction between energy loss and the dynamic response of HSV. To optimize the HSV dynamic response time considering energy loss, the HSV work process simulation model was established in this paper, and the model was verified based on armature lift experimental data. Without changing the structural parameters of the HSV, the four parameters of electroconductibility, spring stiffness, damping coefficient, and coil resistance were selected as the key parameters affecting the dynamic response and energy loss. The response surface models (RSMs) of opening response time, closing response time, eddy current energy and Joule energy of the HSV were constructed by using the smoothing spline-analysis of variance method. The multi-objective cooperation optimization of HSV under the interaction of dynamic response characteristics and energy loss was completed by using non-dominated sorting genetic algorithms. After optimization, the opening and closing response times of HSV were reduced by 15.1% and 16.6% respectively, while the eddy current energy and Joule energy were reduced by 5.2% and 48.4% respectively. In this paper, the dynamic response and energy loss were jointly optimized. The presented results provide theory instruction for multi-objective cooperative optimization of HSV.

The optimization method of CNC lathe performance based on Morris sensitivity analysis and improved GA algorithm

This study achieved the goal of guiding bed design and optimization by conducting multi-objective optimization research on the performance of CNC lathe beds. In this study, Morris analysis was first performed on the sensitivity of the parameters, and then out to optimize the parameters using a combination of neural network and genetic algorithm. The loss function value, RMSE error accumulation, recall, sensitivity and specificity of the ASSGA-BP optimization model were better. The maximum error between the predicted and true values of the ASSGA-BP model was 0.28 mm. In the performance study of the multi-objective optimization method based on the Morris sensitivity analysis and the improved GA algorithm, the average MAE value is 0.91 %. The average RMSE value is 0.59 %. Also, the new model is significantly better than the NSGA-II, EGA, and FGA algorithms in terms of both the number of final non-dominated solutions and the speed of reaching convergence. The above results demonstrate that the model proposed in this study has high performance, can achieve faster convergence and has the best stability of the convergence state. The innovation of this article lies in the use of the Morris method to screen and evaluate numerous parameters in order to improve the accuracy of the calculation results and ensure the effectiveness of the optimization results. The improved algorithm overcomes the problems of BP neural network and can effectively improve the generalization performance of the neural network, thereby improving the prediction accuracy of the model.

An evolutionary algorithm based on fully connected weight networks for mixed-variable multi-objective optimization

Multi-objective optimization research has mostly focused on continuous-variable problems. However, real-world optimization problems often involve multiple types of variables (continuous, integer, and discrete) and multiple conflicting optimization objectives, called mixed-variable multi-objective optimization problems (MVMOPs). Discrete variables make the decision space of the problem discrete. In contrast, while different types of variables need different treatments by the evolutionary algorithm, which poses a challenge to the efficient search of the evolutionary algorithm. Therefore, we propose an evolutionary algorithm based on a fully connected weight network (FCWNEA). The fully connected network structure characterizes the entire decision space, the node access count records the frequency of visits to the node, and the weights of connections and the activity of variables estimate the distribution of the decision space. This information assists in generating offspring solutions. To evaluate the performance of the proposed algorithm, we conduct empirical experiments on different types of problems. The results show that the proposed algorithm has a significant advantage in mixed-variable multi-objective problems. Moreover, the proposed algorithm is also quite competitive in continuous problems and can better handle the correlation between variables in optimization problems.

The design and operation optimization for conductive rubber heated snow melting bridge deck pavement

In order to solve the problem of ice and snow disasters on roads, this study proposed a new technology for active deicing of bridge decks using conductive rubber heating system (CRHS) as the heat source. The existing measures to improve the operation efficiency of snowmelt pavement often only optimize 1–2 influencing factors, and lack of optimization of multiple factors such as paving design, operation and environment. Therefore, in order to improve the operation efficiency, this study carried out multi-objective optimization on the design and operation of conductive rubber heated snow melting bridge deck pavement. The bridge deck pavement scheme of conductive rubber heating system was proposed, and its two-dimensional numerical model of heat transfer was established. The laying spacing of conductive rubber (s), heating power (p), ambient temperature (t) and wind speed (v) were used as input parameters for system operation optimization. Predictive models for total heating time (THT) and the loss of energy rate (LER) were developed using response surface methodology (RSM). The specific effects of input parameters on THT and LER and their interactions were analyzed by thermal simulation. Aiming at less THT and lost energy (LE), the optimal input parameters of conductive rubber heating system were obtained by the multi-objective particle swarm algorithm. The results indicated that shorter THT and lower LE could be achieved when the laying spacing (s) was 0.25 m, the ambient temperature (t) was −3 °C, the wind speed (v) was 2 m/s, and the input power was 550 W/m2. And adjusting s was more effective than adjusting p to obtain low THT and LER. The results and recommendations can effectively guide the operation of conductive rubber heating system. The multi-objective optimization research idea of improving the operation efficiency of snowmelt pavement in this paper can provide reference for similar deicing pavement applications.

Multi-Objective Optimization Research on the Integration of Renewable Energy HVAC Systems Based on TRNSYS

Well-designed passive buildings can drastically reduce building energy consumption, and optimal design of air conditioning systems is the key to achieving low operating energy consumption in near-zero energy buildings. TRNSYS was used to build the simulation model for a near-zero-energy building and its air conditioning system in Beijing. The Taguchi method was used to sort the design parameters that affect system performance according to the degree of influence and find the best combination of design parameters to optimize the system, which increased the solar fraction of the system by 4.6% and reduced the annual operating energy consumption by 7.32%. For the optimized system, a multi-objective optimization function of the life cycle costs and carbon emissions was established. By comparing the energy consumption, life cycle costs, and carbon emissions of the air conditioning system under different system configurations, optimal configuration solutions under different design target weights were obtained. It was found that using a ground source heat pump system + solar collector system had better energy-savings benefits, but the operating costs were slightly higher. The application of absorption refrigeration can reduce the system operating costs but will increase the initial investment. The best economic benefits were achieved using the ground source heat pump system + solar collector system for heating in winter and the ground source heat pump system for cooling in summer, and the best environmental benefits were obtaining using the ground source heat pump system + solar collector system for heating in winter and the ground source heat pump system + solar absorption refrigeration system in summer, which provides a reference for the optimization design and research of air conditioning systems in near-zero energy buildings.

Multi-objective Evolutionary Algorithm Supported Construction of Architectural Window Design Model Based on Visual Comfort - A Case Study of Office Buildings in Cold Regions

Abstract With the accelerated advancement of big data Internet development, people gradually realize the urgency and importance of energy saving, so the multi-objective optimization research for the performance of office buildings in cold regions is significant, and this paper focuses on the construction of a building window design model based on visual comfort supported by multi-objective evolutionary algorithm- taking office buildings in cold regions as an example, firstly, by reviewing the literature to understand the principle, algorithm, and concept of multi-objective evolution, construct the building window design model based on visual comfort according to the objective function and extract the standard model of inner corridor slab space building in office buildings in cold regions of Harbin and Shenyang. The optimized design of building window parameters was carried out using the established joint simulation and optimization work platform. The results show that for Harbin, as the window heat transfer coefficient increases, the heating energy consumption increases, the cooling energy consumption increases more slowly, and the total energy consumption increases linearly. An increase in window heat transfer coefficient by 0.1W/ (m 2 ∙K) Increases are cooling energy consumption by 0.18%, heating energy consumption by 0.78%, and total energy consumption by 0.46%. For Shenyang, as the window heat transfer coefficient increases, the heating energy consumption increases, the cooling energy consumption increases more slowly, and the total energy consumption increases linearly. If the heat transfer coefficient of the window increases by 0.1W/ (m 2 ∙K), cooling energy consumption increases by 0.18%, heating energy consumption increases by 0.78%, and total energy consumption increases by 0.46%. This study provides a theoretical basis for extracting standard models for other building types while making the results more generalizable and improving the efficiency of sustainable office building design.

Multi-objective Optimal Scheduling Analysis of Power System Based on Improved Particle Swarm Algorithm

Economic Environmental Dispatching (EED) in power systems is a multi-variable, strongly constrained, non-convex, multi-objective optimization problem that is difficult to properly handle using traditional methods. However, the application of particle swarm optimization algorithms may result in insufficient population diversity and easy to fall into local optimization problems. Therefore, this paper proposes an adaptive backbone multi-objective particle swarm optimization (ABBMOPSO) method to solve the economic and environmental scheduling problems of power systems. This paper first analyzes the topology and computational flow of particle swarm optimization algorithms, and then constructs a multi-objective optimization research framework that integrates Pareto optimization principles for the scheduling of power generation units. The execution algorithm is the improved multi-objective particle swarm optimization algorithm (MOPSO). This paper establishes a mathematical model for the economic and environmental scheduling of power systems, which optimizes conflicting fuel cost functions and pollutant emission functions simultaneously, taking into account nonlinear constraints such as load balance constraints and unit operation constraints. The improved ABBMOPSO algorithm is used to optimize the solution to improve the global search ability of the EED model. The simulation data of seven units show that the ABBMOPSO algorithm has a minimum power generation cost of 588.1 $/h and a minimum pollutant emission of 0.192 t/h, which is significantly superior to other algorithms and reduces the number of iterations, with good feasibility.

Numerical studies on heat extraction evaluation and multi-objective optimization of abandoned oil well patterns in intermittent operation mode

Geothermal is an important renewable energy source, but the high cost of drilling limits its popularization. The oilfield area is rich in geothermal resources and has a large number of high-temperature abandoned wells. It is an economic and effective method to transform the abandoned wells into geothermal wells. At present, the heat extraction research of abandoned wells mostly focuses on single-well closed systems, while the most common in oilfields is the well patterns open system, which involves the flow and heat extraction of oil-water multiphase. More, the main utilization mode of oilfield geothermal is heating, which is an intermittent operation, and the existence of the heat recovery period makes the solution process more complicated compared to continuous production. Therefore, the numerical simulation of oil-water two-phase and the scheme optimization under intermittent operation are the key problems in the current oilfields geothermal research, that is, it should be made clear what the effects of operation parameters are and how to get the optimization design for the intermittent mining of heat-oil cogeneration from abandoned wells. For this reason, the oil-water two-phase heat-flow coupling model and economic evaluation model are established. Then, database is established through parameter sensitivity analysis, and multi-objective optimization research is carried out on the lifetime and economic benefits using Non Sorting Genetic Algorithm II (NSGA-II). The results show that economic benefit and end-point temperature of the optimized scheme are enhanced by 6.75 million US dollars and 7.10 K after 15-year production, respectively. The heat-oil cogeneration performance is superior, so the influence of oil phase cannot be ignored in the heating process of the well patterns system for oilfield geothermal production.

Evaluation of Thermal Insulation Performance of Building Exterior Wall Based on Multiobjective Optimization Algorithm

A complete evaluation of conflicting and nonlinear multiperformance is referred to as an evaluation system. Therefore, multiobjective optimization research for building performance is of great importance. Considering the above problems, this paper proposes a multiobjective performance optimization platform at the architectural design stage. The optimization research is combined with the basic algorithms. It is based on Grasshopper software. It aims to provide a fast and effective method for the optimization design of sustainable buildings. It is important to note that this workflow has no restrictions on what to optimize for or what to target, and can be determined on a case-by-case basis. To evaluate the use of the work platform and make the research results more extensive, the standard model of the inner corridor space building in office buildings in cold areas was extracted. The extracted standard model was taken as the final research object. At the same time, the convergence, distribution, and comprehensive evaluation indexes of the simulation results were analyzed. The quantitative influence parameter system of window parameters on the total building energy consumption, lighting uniformity, and glare probability was established. A series of parametric design models based on the comprehensive optimal solution of building energy consumption and lighting quality was obtained. The main purpose of this paper is to design a green building design method based on the principle of goal-oriented design. This paper, combined with the group search function of nondominated genetic algorithm (NSGA-II), the energy consumption of the building, and the evaluation function of indoor thermal and humid environment, is obtained and linked with MATLAB. The energy consumption of residential buildings and indoor thermal comfort were taken as the optimization objectives of the design scheme. The automatic optimization model of the architectural design scheme was established. Finally, the Pareto solution set of the architectural design scheme is obtained, and the designer can select the appropriate design scheme of the Pareto solution set according to the actual situation of the project, to meet the requirements of green building design. The design scheme optimized by this method can effectively reduce building energy consumption and improve indoor thermal comfort.

Design of a Highly Efficient 20-kW Inductive Power Transfer System With Improved Misalignment Performance

Due to the urgent desire for a fast, convenient, and efficient battery charging technology for electric vehicle (EV) users, extensive research has been conducted into the design of high-power inductive power transfer (IPT) systems. However, there are few studies that formulate the design as a multiobjective optimization (MOO) research question considering both the aligned and misaligned performances and validate the optimal results in a full-scale prototype. This article presents a comprehensive MOO design guideline for highly efficient IPT systems and demonstrates it by a highly efficient 20-kW IPT system with the dc–dc efficiency of 97.2% at the aligned condition and 94.1% at 150-mm lateral misalignment. This achievement is a leading power conversion efficiency metric compared to IPT EV charging systems disseminated in today’s literature. Herein, a general analytical method is proposed to compare the performances of different compensation circuits in terms of the maximum efficiency, voltage/current stresses, and misalignment tolerance. An MOO method is proposed to find the optimal design of the charging pads, taking the aligned/misaligned efficiency and area/gravimetric power density as the objectives. Finally, a prototype is built according to the MOO results. The charging pad dimension and total weight, including the housing material, are 516*552*60 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /25 kg for the transmitter and 514*562*60 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /21 kg for the receiver. Correspondingly, the gravimetric, volumetric, and area power density are 0.435 kW/kg, 581 kW/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , and 69.1 kW/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively. The measured efficiency agrees with the anticipated value derived from the given analytical models.

A Learning-based Innovized Progress Operator for Faster Convergence in Evolutionary Multi-objective Optimization

Learning effective problem information from already explored search space in an optimization run, and utilizing it to improve the convergence of subsequent solutions, have represented important directions in Evolutionary Multi-objective Optimization (EMO) research. In this article, a machine learning (ML)-assisted approach is proposed that: (a) maps the solutions from earlier generations of an EMO run to the current non-dominated solutions in the decision space ; (b) learns the salient patterns in the mapping using an ML method, here an artificial neural network (ANN); and (c) uses the learned ML model to advance some of the subsequent offspring solutions in an adaptive manner. Such a multi-pronged approach, quite different from the popular surrogate-modeling methods, leads to what is here referred to as the Innovized Progress (IP) operator. On several test and engineering problems involving two and three objectives, with and without constraints, it is shown that an EMO algorithm assisted by the IP operator offers faster convergence behavior, compared to its base version independent of the IP operator. The results are encouraging, pave a new path for the performance improvement of EMO algorithms, and set the motivation for further exploration on more challenging problems.

Modeling and optimization of high temperature proton exchange membrane electrolyzer cells

ABSTRACT Although high-temperature proton exchange membrane electrolyzer cells (HT-PEMECs) have been promising devices to store energy in recent years, the effect of certain parameters on their performance is still unclear. Therefore, a 2D multiphysics model is adopted to study the related processes of electrochemical reactions in an HT-PEMEC. The model is validated by comparison with electrochemical experimental data. Subsequently, the effects of applied voltage, anode water mass fraction, anode gas velocity, and cathode gas velocity on the multiphysics are studied, and the trends of efficiency and conversion rate are analyzed. Thermoneutral voltage is observed through a parametric study. Moreover, the maximum energy efficiency (54.5%) is obtained by optimizing the operating conditions. This study can be regarded as a foundation for the subsequent control and multi-objective optimization research.

Research on coordination design method of main bearing assembly structure based on the maximum radial deformation of bearing bush

Aiming at the difficulty of control and evaluation of main bearing deformation in the coordination design of the main bearing assembly structure for a high-speed diesel engine, taking MRD (the MRD means the maximum radial deformation of the bearing bush) of the bearing bush as an index to evaluate the out-of-round deformation of the bearing bush was proposed in this paper. The numerical calculation method of the MRD was given and the correctness of the method was experimentally verified. And the influence rules of different design parameters on the MRD were analyzed. On this basis, the coordination multi-objective optimization research of the main bearing assembly structure was carried out, and the optimization results were analyzed based on the influence rules of different design parameters on the reliability indexes. The results show that, when the pre-tightening force of the vertical bolt and the bearing bush interference are 240 kN and 0.17 mm respectively, the MRD reaches the minimum value. If the two values continue to increase, redundant loads can be generated, leading to the increase of the MRD. After optimization, the engine block strength coordination and bearing cap strength coordination had increased by 2.47% and 10.48%, respectively, and the deformation coordination and contact coordination had increased by 46.15% and 14.84%, respectively.

Multi-objective Optimization of Three-shaft Gas Turbine for Sudden Unloading

Considering the requirements of economy, emission and dynamic performance of three-shaft gas turbine for sudden unloading, the multi-objective optimization of three-shaft gas turbine performance was carried out. Firstly, the simulation model of three-shaft gas turbine was established, and the accuracy of the model was verified by comparing with the design point parameters. Then, the research of multi-objective optimization of the economy, emission and dynamic performance for sudden unloading is carried out based on the three-shaft gas turbine model, and the TOPSIS method was used to select the optimal point in the Pareto solution set. This paper provides a theoretical basis for the performance improvement and optimization design of the three-shaft gas turbine.

Multi-objective Structural Optimization of VL Seal Ring Based on Isight

To obtain the best geometric size of VL sealing ring and improve its sealing performance, this paper carried out multi-objective optimization research on VL sealing ring by Isight software. Firstly, according to the working principle and geometric size of the VL seal ring, determined the constraint conditions, established the optimization objective equation, and given the value range of the structural parameters was. Then, verified the validity of the approximate model by fitting the radial basis neural network model provided. Finally, analyzed the sensitivity of influencing factors of oil film thickness at lip, and established the optimization module by NSGA-II multi-objective optimization algorithm. As a result, the best geometric size is obtained from multi-objective optimization, and the structure of VL seal ring has achieved the goal of more suitable high speed dynamic seal and longer service life.

Multiparameter and Multiobjective Optimization Design Based on Orthogonal Method for Mixed Flow Fan

Optimization design of an impeller is critical for the energy performance of a fan. This paper takes the promotion of fan efficiency and pressure rise as the optimization objectives to carry out multiparameter and multiobjective optimization research. Firstly, an experimental test bench is built to measure the energy performance of the original fan and verify the accuracy of the numerical method. Then, the hub outlet angle of impeller β1, the impeller outlet angle increment Δβ1, the wrap angle φ, the hub outlet angle of diffuser β2, and the diffuser outlet angle increment Δβ2 are set as the optimal parameters to conduct orthogonal optimal design. The results show that the efficiency of the optimal fan increases by 11.71%, and the pressure rise increases by 50.15%. The pressure and velocity distributions in an optimal fan are uniform, the internal flow separation is weakened, and the influence of tip leakage flow is reduced, which makes for the improvement of energy performance for the fan.

Multiobjective optimization research on the response time of a pneumatic pilot-operated high speed on/off valve

In order to solve the contradiction between the opening and closing time of the high speed on/off valve for space propulsion systems of the liquid rocket engine, a pneumatic pilot-operated high speed on/off valve is proposed and multiobjective optimization for the opening and closing time of the valve is carried out in this paper. Based on the analysis of the working mechanism of the valve, the mathematical models for the pilot valve and the main valves are established respectively. The Plackett-Burman design is used to select the optimization variables which influence the performance of the response time significantly. The central composite design is used to obtain the sample points and establish second-order response surface models of the response time. The NSGA-II is used to obtain the Pareto front of the optimization objectives. The optimized opening and closing time can be reduced by 17.7% and 37.4% respectively. A prototype based on the optimized parameters is manufactured and tested to verify the accuracy of the multiobjective optimization results. The test results verify the validity of the optimization approach for the proposed valve in this paper.