摘要：Drop-on-demand (DOD) bioprinting has been widely used in tissue engineering due to its highthroughput efficiency and cost effectiveness. However, this type of bioprinting involves challenges such as satellite generation, too-large droplet generation, and too-low droplet speed. These challenges reduce the stability and precision of DOD printing, disorder cell arrays, and hence generate further structural errors. In this paper, a multi-objective optimization (MOO) design method for DOD printing parameters through fully connected neural networks (FCNNs) is proposed in order to solve these challenges. The MOO problem comprises two objective functions: to develop the satellite formation model with FCNNs;and to decrease droplet diameter and increase droplet speed. A hybrid multi-subgradient descent bundle method with an adaptive learning rate algorithm (HMSGDBA), which combines the multisubgradient descent bundle (MSGDB) method with Adam algorithm, is introduced in order to search for the Pareto-optimal set for the MOO problem. The superiority of HMSGDBA is demonstrated through comparative studies with the MSGDB method. The experimental results show that a single droplet can be printed stably and the droplet speed can be increased from 0.88 to 2.08 m·s^-1 after optimization with the proposed method. The proposed method can improve both printing precision and stability, and is useful in realizing precise cell arrays and complex biological functions. Furthermore, it can be used to obtain guidelines for the setup of cell-printing experimental platforms.

摘要：Tolerance design plays an important role in reliability design for electronic circuits. The traditional method only focuses on the consistency of output response. It is not able to meet the needs of increasing development of electronic products. This paper researches the state of related fields and proposes a method of multi-objective reliability tolerance design. The characteristics of output response and operating stresses on critical components are both defined as design objectives. Critical components and their operating stresses are determined by failure mode and effect analysis （FMEA） and fault tree analysis （FTA）. Sensitivity analysis is carried out to determine sensitive parameters that affect the design objectives significantly. Monte Carlo and worst-case analysis are utilized to explore the tolerance levels of sensitive parameters. Design of experiment and regression analysis are applied in this method. The optimal tolerance levels are selected in accord with a quality-cost model to improve consistency of output response and reduce failure rates of critical components synchronously. The application in light-emitting diode （LED） drivers indicates details and potential. It shows that the proposed method provides a more effective way to improve performance and reliability of electronic circuits.

摘要：This paper considers the problem of optimal multi-objective trajectory design for autonomous rendezvous. Total velocity cost and relative state robustness of close-looped control are selected as the objective functions. Based on relative dynamics equations, the state equations and measurement equations for angles-only relative navigation between spacecraffs are set forth. According to the method of linear covariance analysis, the close-looped control covariance of the true relative state from the reference relative state is analyzed, and the objective functions of relative state robustness are formulated. Considering the total velocity cost and the relative state robustness, the multi-objective optimization algorithm of NSGA-II is employed to solve this multi-impulsive rendezvous problem. Lastly, the validity of the objective functions and the covariance results are demonstrated through 1 00 times Monte Carlo simulation.

摘要：In view of incomplete probability information multi-objective question, it used probabilistic perturbation method and Edgeworth series technique to study reliability optimization design. The first four moments of basic random variables are known under condition. It used the Ant Colony Algorithm to design cutting head roadheader, the optimized result indicated that cutting head load fluctuation and compared energy consumption were reduced obviously at the same time. This result enhanced roadheader operational reliability and energy effectively.

摘要：multi-objective optimization linked with an urban stormwater model is used in this study to identify cost-effective low impact development (LID) implementation designs for small urbanizing watersheds. The epsilon-Non-Dominated Sorting Genetic Algorithm II (ε-NSGAII) has been coupled with the US Environmental Protection Agency’s Stormwater Management Model (SWMM) to balance the costs and the hydrologic benefits of candidate LID solutions. Our objective in this study is to identify the near-optimal tradeoff between the total LID costs and the total watershed runoff volume constrained by pre-development peak flow rates. This study contributes a detailed analysis of the costs and benefits associated with the use of green roofs, porous pavement, and bioretention basins within a small urbanizing watershed inState College,Pennsylvania. Beyond multi-objective analysis, this paper also contributes improved SWMM representations of LID alternatives and demonstrates their usefulness for screening alternative site layouts for LID technologies.

摘要：Many engineering design problems are characterized by presence of several conflicting objectives. This requires efficient search of the feasible design region for optimal solutions which simultaneously satisfy multiple design objectives. Genetic algorithm optimization (GAO) is a powerful search technique with faster convergence rates than traditional evolutionary algorithms. This paper applies two GAO-based approaches to multi-objective engineering design and finds design variables through the feasible space. To demonstrate the utility of the proposed methods, the multi-objective design of an I-beam will be presented.

摘要：Aerodynamic performance of low-Reynolds-number high-lift airfoil makes a great impact on designing a high-efficiency propeller for low-dynamic vehicles in stratosphere. At high altitude,low-Reynolds-number airfoils are supposed to have high lift-drag ratio or high endurance factor at cruising attack angle along with good stall characteristics. To design such a high-performance low-Reynolds-number high-lift airfoil,the paper established a hierarchical multi-objective optimization platform by combing direct search optimization algorithm EXTREM and airfoil flow field solver XFOIL to automatically and quickly calculate aerodynamic performance function of airfoil by computer. It provides an effective solution to multi-point design problem of low-speed low-Reynolds-number airfoil. It can be seen from the results of three typical optimization examples,the new airfoil E387_OPT2,FX63-137_OPT2 and S1223_OPT2 based on hot low-Reynolds-number high-lift airfoils (Eppler 387 airfoil,Wortmann FX63-137 airfoil and S1223 airfoil) can meet the optimization design requirements and have very good aerodynamic characteristics in both design state and non-design state. Thus,the applicability and effectiveness of hierarchical multi-objective optimization platform are verified.

摘要：In this paper, we present an investigation on the tracking performances of feedback control as a function of reference signals. We use multi-objective optimal designs of feedback controls as a fair basis for comparing different control designs, and examine step, ramp, and periodic signals at various frequencies. Through comparing the tracking performances of controls designed with different reference signals,we find that the controls designed with ramp signals perform better in tracking step and ramp references than those designed with step signals. To track periodic signals, we find that the controls designed with periodic signals at the same frequency generally provide the best performance, and those designed with step and ramp signals perform comparably.

摘要：This paper presents an optimal vehicle and seat suspension design for a half-car vehicle model to reduce human-body vibration (whole-body vibration). A genetic algorithm is applied to search for the optimal parameters of the seat and vehicle suspension. The desired objective is proposed as the minimization of a multi-objective function formed by the combination of seat suspension working space (seat suspension deflection), head acceleration, and seat mass acceleration to achieve the best comfort of the driver. With the aid of Matlab/Simulink software, a simulation model is achieved. In solving this problem, the genetic algorithms have consistently found near-optimal solutions within specified parameters ranges for several independent runs. For validation, the solution obtained by GA was compared to the ones of the passive suspensions through sinusoidal excitation of the seat suspension system for the currently used suspension systems.

摘要：This paper presents a systematic approach of optimal design and control of a surface-mount, permanent-magnet synchronous torque motor for the next-generation machine tools. A step-by-step procedure of optimization integrates multiple performance objectives and constraints to help the designer make the best decision on the final motor geometry from both design and control perspectives. In the perspective of design, a torque motor with concentrated windings and similar numbers of slots and poles may achieve the desired performance after optimization for multiple objectives, leading to a sinusoidal flux density for a nearly ripple-free torque distribution. From the control perspective, an optimal current waveform with an ideal shift angle is determined for each phase by aligning the current excitation with the back electromotive force. Both design and control of the surface-mount, permanent magnet machine are verified by the finite element method, and a prototype is fabricated for performance validation.