Integrated Optimization Method Research Articles

Integrated optimization method for helical gear hobbing parameters considering machining efficiency, cost and precision

The formulation of dry hobbing processing parameters depends heavily on mass experiments and the experiences of skilled technicians, and the use of unsuitable parameters will lead to high cost, low efficiency and severe precision defects. To resolve this issue, a helical gear processing parameter optimization method (PPOM-HG) is proposed in this paper. First, an efficiency-cost-accuracy triple-target optimization model is established. A manufacturing efficiency model is established through a detailed analysis of the geometric trajectory of a hob. A helical gear manufacturing cost model is established by analyzing the power curve of the hobbing machine and the hob’s lifetimes under various processing parameters. A modified correlation analysis random forest (CARF) model is designed for predicting gear machining precision, which replaces the traditional empirical precision function. Then, for searching for the optimal solution of the established efficiency-cost-precision triple-target model, an adaptive multiobjective fusion evolutionary algorithm (AMFEA) with adaptive evolution parameters is proposed. Finally, via many helical gear machining experiments, the validity and advantages of the proposed CARF and AMFEA methods are demonstrated, and the selection strategy of the Pareto front solution under various conditions is discussed.

Multiobjective Optimization of Cable Forces and Counterweights for Universal Cable-Stayed Bridges

In cable-stayed bridges, especially asymmetric bridges, counterweights are always made to work together with cable pretension forces to get a reasonable finished state. To solve the optimization problem of the cable-stayed bridge considering the counterweights, the integrated optimization method (IOM) for estimating cable forces and counterweights is proposed. In this method, the counterweights are proposed to act on the anchor points. After that, the summary of the minimum weighted total bending energy and the summary of the counterweights are considered as two objective functions of a multiobjective problem. Finally, the dynamic weighted coefficient method is used to solve this problem and realize the Pareto solution set. IOM presents detailed procedures in a simple numerical model and is then applied to the Yong-ding special-shaped cable-stayed bridge. The results show that not only IOM can realize the priority selection of the loading position of the counterweights but also get a better reasonable finish state because of the introduction of the counterweight dimension; the dynamic weighted coefficient method can quickly find the Pareto optimal solution set and be further screened by decision-makers; counterweight is very helpful to reduce torsion and other spatial effects in cable-stayed bridges. IOM can be used as a universal optimization method for cable-stayed bridges.

Integrated Optimization for Commuting Customized Bus Stop Planning, Routing Design, and Timetable Development With Passenger Spatial-Temporal Accessibility

The customized bus (CB) is an innovative type of transit service that can provide a personalized efficient transit services for passengers and environmental friendliness and congestion alleviation in metropolitan areas. This work develops an integrated optimization method for CB stop deployment, route design, and timetable development optimization problems while meeting travel demands as much as possible to obtain system-optimal CB service plans. Through the perspective of space-time network, the CB service design problem (CBSDP) is formulated as an integrated optimization model with the objectives of maximizing passenger accessibility and minimizing operating cost. An inconvenience index of passengers is introduced in the problem to measure the service quality, and the total number of stops for all involved CB routes is set as one of the objectives to optimize the total cost of the CB system. A heuristic approach is applied to generate efficient solutions for the CBSDP. Two types of instance, namely, a numerical experiment and a real-world instance, are implemented to demonstrate the performance of the proposed method. We also conduct a series of sensitive analyses to explore the influences of various parameters on the CB system for capturing the interaction among stops, routes, and timetables. Final results show that the CB plans obtained by the proposed method can provide efficient services by balancing passenger convenience and operating cost.

Optomechanical structure design and experiment of high-resolution video camera for micro-nano satellite(Invited)

The design, assembly and experiment of the optomechanical system of a compact spaceborne video camera developed for the 20 kg micro-nano optical remote sensing satellite were introduced, and the integrated optimization method was also proposed. The camera was a Cassegrain optical system including two mirrors and one corrector assembly. In order to obtain the best thermal stability, the mirrors were made of silicon carbide. Firstly, based on the task and overall design of 20 kg micro-nano video satellite, the requirements of video camera were proposed; Then, the optical and optomechanical structure system of the video camera were introduced respectively; In order to further improve the lightweight rate, while meeting the requirements of optical performance, the optomechanical integration optimization method was used to the lightweight design. After optimization, the mass of the optomechanical system was only 3.03 kg, the weight of the whole camera was only 4.76 kg, and the 1st mode was larger than 120 Hz; Finally, the assembly and ground mechanical experiments of the camera were summarized. The results showed that the camera had very dynamic properties and stability.

A scheduling and control integration optimization method for regenerative braking energy utilization

The utilization of regenerative braking energy is of great significance to the energy saving of subway. Therefore, this paper proposes an optimization method for scheduling and control integration, which not only adjusts the timetable but also optimizes the speed curves of trains. When there is a train braking, this method will try to find a train that accelerates to absorb the regenerative energy generated by the braking train. Firstly, this paper establishes the timetable energy saving optimization model, based on which the speed curves will be optimized. Furthermore, we design a scheduling optimization algorithm based on genetic algorithm, and optimizes speed curves of trains by binary search method to obtain a good solution. Finally, simulations are given using the real data of Beijing Metro Line 4 to evaluate the proposed method, and the results show that the integrated scheduling and control optimization method can reduce energy consumption by 15.18%. In the random disturbance simulations, the proposed method shows good robustness, which makes it possible to apply this method to the real subway operations.

Integrated Optimization of Bus Route Adjustment With Passenger Flow Control for Urban Rail Transit

The contradiction between transport capacity and passenger demand in urban rail transit is usually prominent during peak hours in some megacities of China, and some passenger flow control measures have been adopted to alleviate passenger congestion. To better save passengers’ travel time when taking passenger flow control measures, this paper proposes an integrated optimization method of bus route adjustment with network-level passenger flow control for urban rail transit, in which the controlled passengers can freely choose to shift to bus or to retain in urban rail transit for pursuing a lower travel cost. With the objectives of minimizing average additional travel time for all affected passengers and maximizing the operating revenue of urban rail transit, an integer non-linear programming model is formulated to determine the inbound passenger volumes and bus adjustment schemes. To solve this proposed model effectively, a multi-objective particle swarm optimization based on dual-population co-evolution is designed. Finally, three sets of numerical experiments, including an integrated optimization experiment and two independent optimization experiments of passenger flow control, are implemented to demonstrate the feasibility and benefits of the proposed method.

Exact Modeling and Multiobjective Optimization of Vernier Machines

This article presents a high-fidelity analytical solution and a fast integrated optimization method for vernier machines. First, a harmonic modeling method (HMM) is adopted to obtain the magnetic field distribution of vernier machines. Particularly, the stator teeth of vernier machines are divided into several blocks to take account of the local magnetic saturation of soft-magnetic material. Then, the electromagnetic parameters, such as the average torque, torque ripple, and efficiency, can be calculated via the analytical model. Next, the proposed harmonic model is combined with the nondominated sorting genetic algorithm II (NSGA-II) to determine a multiobjective optimal design for the studied vernier machine. This integrated optimization method can save computation time, because the data interaction process between the finite-element analysis (FEA) software and the optimization software is eliminated. Finally, the performances of the optimal case are verified by both FEA and experiments with the prototype.

A Fast Optimization Scheme of Coaxial Magnetic Gears Based on Exact Analytical Model Considering Magnetic Saturation

This article presents a harmonic modeling method (HMM) for coaxial magnetic gears (CMGs) by considering the nonlinear B – H curve for soft-magnetic material. CMGs are divided into several annular layers, where Maxwell equation is solved. A convolution matrix of permeability is introduced to obtain the relationship between the magnetic flux density and magnetic field strength, and the continuous boundary conditions can be further satisfied. Subsequently, the magnetic field distribution and electromagnetic performance of CMGs can be calculated by solving the system equations. The computation time and accuracy of HMM are compared with those of finite element analysis (FEA) and subdomain method. The results show that the HMM is relatively fast and precise, and its computation time is about 80% of that of FEA. Then, the parameters of CMGs are optimized based on the HMM and non-dominated sorting genetic algorithm II (NSGA-II) combined optimization method. Particularly, the modulator is divided into multilayers, so its saturation can be modeled more precisely, and its shape can be further optimized. Finally, two optimal cases are evaluated and compared, and the result proves the effectiveness of the integrated optimization method.

Obstacle Avoidance and Multitarget Tracking of a Super Redundant Modular Manipulator Based on Bezier Curve and Particle Swarm Optimization

A super redundant serpentine manipulator has slender structure and multiple degrees of freedom. It can travel through narrow spaces and move in complex spaces. This manipulator is composed of many modules that can form different lengths of robot arms for different application sites. The increase in degrees of freedom causes the inverse kinematics of redundant manipulator to be typical and immensely increases the calculation load in the joint space. This paper presents an integrated optimization method to solve the path planning for obstacle avoidance and discrete trajectory tracking of a super redundant manipulator. In this integrated optimization, path planning is established on a Bezier curve, and particle swarm optimization is adopted to adjust the control points of the Bezier curve with the kinematic constraints of manipulator. A feasible obstacle avoidance path is obtained along with a discrete trajectory tracking by using a follow-the-leader strategy. The relative distance between each two discrete path points is limited to reduce the fitting error of the connecting rigid links to the smooth curve. Simulation results show that this integrated optimization method can rapidly search for the appropriate trajectory to guide the manipulator in obtaining the target while achieving obstacle avoidance and meeting joint constraints. The proposed algorithm is suitable for 3D space obstacle avoidance and multitarget path tracking.

Research on the Optimization Method of Regional Logistics Distribution Integration Based on Intelligent Information Processing

At present, the integration method of logistics distribution does not exchange regional logistics distribution information, the related logistics distribution information is not timely understood, and the integration effect of regional logistics distribution is poor, resulting in long distribution time, high distribution cost and small distribution scope. Therefore, the intelligent information processing technology is introduced to optimize the regional logistics distribution integration method. This paper established a regional logistics distribution integration mechanism which is able to process logistics distribution integration information through hierarchical authorization, transform the business data between the upstream and downstream enterprises in the supply chain of the regional logistics canter and integrate vehicle resources and human resources to optimize regional logistics distribution based on intelligent information processing. Taking the distribution time, distribution cost and distribution distance as experimental indicators, the experiment is carried out. The results show that the logistics distribution time, cost and distance of the proposed integrated optimization method are better than those of the traditional methods.

An integrated multi-objective optimization approach to determine the optimal feature processing sequence and cutting parameters for carbon emissions savings of CNC machining

ABSTRACT Carbon emissions caused by CNC machining attract increasing attentions. It has been proved that carbon emissions and processing time are jointly affected by process routes and cutting parameters. Cutting parameters and process routes optimization, however, are traditionally carried out separately and sequentially, limiting the potential of carbon emissions reduction. To further reduce carbon emissions, an integrated approach of features processing sequence and cutting parameters optimization is proposed to minimize the carbon emissions and processing time. Firstly, a multi-objective optimization model for minimizing carbon emissions and processing time is proposed. Secondly, an NAGS-II algorithm is designed to solve the proposed model. Then, case studies are conducted to verify the feasibility and validity of the proposed integrated optimization approach. Case studies and comparative analysis with other literature show that: i) integrated optimization of features processing sequence and cutting parameters can achieve more carbon emissions savings than features processing sequence optimization with preset cutting parameters; ii) the proposed multi-objective integrated optimization method can strike a trade-off between carbon emissions and processing time.

Lightweight Design of Automotive Front End Material-Structure Based on Frontal Collision

<div class="section abstract"><div class="htmlview paragraph">The front end structure is an important role in protecting the vehicle and passengers from harm during the collision. Increasing its protective capacity can be achieved by increasing the thickness or replacing high-strength materials. Most of the current research is analyzed separately from these two aspects. This paper proposes a multi-objective optimization method based on weighting factor analysis, which combines material and thickness selection. Firstly, the optimized components are determined based on the 100% frontal collision simulation results. Secondly, six thicknesses and two materials of the front part of the vehicle body are selected as design variables to construct an orthogonal test design. In this paper, a weight-based multi-factor optimization method is used to numerically analyze the response results obtained by orthogonal experiments. Analyze the impact of each factor on the optimization goal to select the most reliable optimization. This optimization method can select the best material and component thickness combination scheme. The results show that the mass of the selected parts are reduced by 16.5%; the total energy absorption is increased by 5.2%; the intrusion in the dash is reduced by 8.9%; and the peak acceleration of the B-pillar is reduced by 39.2%.The material-structure integration optimization method is an effective method to solve the contradiction between lightweight and crashworthiness.</div></div>

A new method for improving the tritium breeding and releasing performance of China Fusion Engineering Test Reactor phase II helium‐cooled ceramic breeder blanket

China Fusion Engineering Test Reactor (CFETR) is a brand new Tokamak reactor which is currently being designed to fill the intervals between ITER and future DEMO fusion reactor. It has two operation phases: phase I with Pf = 200 MW is to demonstrate steady-state operation; phase II with Pf = 1000 MW to validate DEMO technology. Helium-cooled ceramic breeder (HCCB) blanket is one of the candidate blanket concepts for CFETR. Until now, there are many research institutes which have performed conceptual designs and comprehensive analyses works for phase I HCCB blanket in detail. However, lately, the operational stage of CFETR has transformed from phase I to phase II, and the latest core design parameters have been just determined (major radius equals to 7.2 m, minor radius equals to 2.2 m). Therefore, the design and analyses work for HCCB blanket should also be initialized. In this work, based on the original integrated optimization method, the radial structure layout optimization of the outboard equatorial phase II HCCB blanket module is conducted by NTCOC. However, the calculation results show that the original optimization method provides inadequately optimized tritium breeding performance and insufficient tritium releasing ability under the condition of high fusion power. To solve these problems, a dynamic feedback is incorporated into the original optimization method for the first time, and then the calculation results show that the problem has been solved well after revision. This work can supply worthy guidance and reference for the conceptual design and comprehensive analyses of CFETR phase II HCCB blanket.

Integrating a Weighted Additive Multiple Objective Linear Model with Possibilistic Linear Programming for Fuzzy Aggregate Production Planning Problems

This study uses an integrated optimization method by applying a weighted additive multiple objective linear model with Possibilistic Linear Programming (PLP) to fuzzy Aggregate Production Planning (APP) problems under an uncertain environment. The uncertainty conditions include uncertainties of operating times and costs, customer demand, labor level, as well as machine capacity. The aim of this study is to minimize total costs of the plan that consist of the production cost and costs of changes in labor level. The proposed hybrid approach minimizes the most possible value of the imprecise total costs, maximizes the possibility of obtaining lower total costs, and minimizes the risk of obtaining higher total costs from PLP as multiple objectives for the fuzzy multiple objective linear model optimization. The outcome of the proposed approach shows that the solution is closer to the ideal solution obtained from Linear Programming than a typical solution obtained from PLP. There is also a higher overall satisfaction value.

Integrated optimization of process planning and scheduling for reducing carbon emissions

In order to reduce environment impacts of manufacturing processes and fill in research gaps that most previous separated optimization of process planning and scheduling ignored influences of process planning on scheduling, a multi-objective integrated optimization model of process planning and scheduling for reducing carbon emissions in manufacturing processes is proposed. The model aims at minimizing makespan and carbon emissions in manufacturing processes by integrated optimizing machining methods for all machining features of workpieces, machine allocations of processes, process routes and machining sequence of workpieces. Because there are many parameters in the proposed model needed to be optimized and they are interactional, a four segment encoding method is designed and a Non-dominated Sorting Genetic Algorithm II is adopted to solve the proposed model. A case study including three workpieces with twenty-three machining features to be processed by turning, milling, drilling, boring and grinding is used to verify the proposed integrated model and algorithm. Results show that the proposed integrated optimization method can further reduce carbon emissions and makespan in manufacturing processes compared with conventional separated optimization of process planning and scheduling. The proposed integrated optimization method is validated.

Integrated Optimization Method with Active Learning for Bi-level Optimization Problems

Integrated Optimization Method with Active Learning for Bi-level Optimization Problems

Research on Integrated Optimization Design Method of High-Efficiency Motor Propeller System for UAVs With Multi-States

With the popularity of electric multi-rotor unmanned aerial vehicles (EMRUAV), the problem of low efficiency of the motor propeller system (MPS) becomes serious especially for EMRUAVs which have multiple working states. To solve this problem, an integrated optimization design method for high-efficiency MPS is proposed using an improved parallel particle swarm optimization and differential evolution (PSODE) hybrid algorithm. First, the brushless motor as well as the variable-speed and the adjustable-pitch propellers is modelled. Then considering the characteristics of the motor and the propeller sufficiently, an integrated optimization method for variable-speed and adjustable-pitch MPS is proposed by utilizing the improved PSODE. Specially, the self-organizing feature map theory is added to substitute the traditional “winner-take-all” method in the interactive study of the improved PSODE. Next, the optimization of a small electric-powered tilt quad rotor (TQR) which has four states: hovering, cruising, transition and maximum speed is carried out. At last, the wind-tunnel experiments are implemented to verify the feasibility and effectiveness of the proposed optimization method. The results indicate that the adjustable-pitch MPS is more suitable for EMRUAVs which have multiple working states, and that the improved PSODE has faster convergence speed and better global search ability than other optimization methods.

Integrated Optimization Method with Active Learning Using Iteratively Produced Input-Output Data

Integrated Optimization Method with Active Learning Using Iteratively Produced Input-Output Data

Aerodynamic Optimal Design for Horizontal Axis Wind Turbine Airfoil Using Integrated Optimization Method

In this work, a new approach for aerodynamic optimization of horizontal axis wind turbine (HAWT) airfoil is presented. This technique combines commercial computational fluid dynamics (CFD) codes with differential evolution (DE), a reliable gradient-free global optimization method. During the optimization process, commercial CFD codes are used to evaluate aerodynamic characteristics of HAWT airfoil and an improved DE algorithm is utilized to find the optimal airfoil design. The objective of this research is to maximize the aerodynamic coefficients of HAWT airfoil at the design angle of attack (AOA) with specific ambient environment. The airfoil shape is modeled by control points which their coordinates are design variables. The reliability of CFD codes is validated by comparing the analytical results of a typical HAWT airfoil with its experimental data. Finally, the optimal design of wind turbine airfoil is evaluated about aerodynamic performance in comparison with existing airfoils and some discussions are performed.

Multi-objective layout optimization for an orbital propellant depot

The overall layout optimization design of an orbital propellant depot involves the optimization of shape, size, and positions of propellant tanks in functional module and the optimization of positions of equipment in service module, with the aim of making the carrying capacity of propellant, dry/wet ratio, and mass properties meet the allowable values. To alleviate the difficulty in dealing with the overall optimization problems involving two modules of the orbital propellant depot, a step-by-step modeling and solving strategy is presented. Two multi-objective optimization mathematical models for the tanks in functional module (model I) and the equipment in service module (model II) are constructed separately, which are solved one after another. In the solution process of the two models, model I is solved firstly and the obtained optimization solution is transmitted to model II as a known condition. We mainly focus on the layout optimization of equipment in the service module and give a batch component assignment and layout integration optimization method. In the proposed method, all the components are grouped firstly according to the functional subsystem, and then the obtained component groups are sorted in descending order of their feature values. Finally, the sorted component groups are added into the service module one by one for both assignment optimization and layout optimization. The computational results of the case study show that the obtained Pareto solutions meet the given allowable values of carrying capacity of propellant, dry/wet ratio, and mass properties of the orbital propellant depot.