从龙粳8号的选育看利用综合育种技术实现多优集成

１９８２年选用适宜寒地生态条件的骨干亲本松前和雄基９号，进行第一次杂交后，选出早熟优质的稳定品系龙交８２１３３做为中间亲本材料。为进一步导入抗稻瘟病基因源，１９８７年选用高度抗病的桥梁亲本Ｎ１９３－２（城堡２号／Ｓ５６）进行第二次杂交。１９８８年以Ｆ１作花培，经过１２年连续选择、５年系统培育、特性鉴定和中间试验，运用综合育种技术育成了集早熟、优质抗病、耐冷、丰产、适应性广于一体的寒地早粳多优集成新品种龙粳８号。

选育水稻多优集成新品种的重要途径和方法

采用理想株型、丰产、优质、抗病、抗冷性较好的青系96 为母本与籼粳交中间类型的丰产抗病BG902 杂交,利用籼粳杂种优势和地理远缘优势把高产、优质、抗病、抗冷聚集在一起,在其F3 不稳定株系中选择高产、优质、多抗株系再与株型好、丰产、优质、抗冷的下北杂交,经过多代系统选育、特性鉴定和中间试验的综合育种技术,多学科协作,育成了集高产、优质、抗病、抗冷和广泛适应性于一体的多优集成新品种超产1 号,这是我省水稻育种史上的一次飞跃。

从绥粳5号的育成探索水稻育种多优集成的可行性

用丰产、抗病、优质的藤系 137为母本与抗盐碱、耐冷凉、抗倒伏、熟期早的绥粳 1号为父本进行杂交 ,聚集亲本优良性状 ,去其不良性状 ,经过田间系统选育、特性鉴定与室内考种 ,育成了集丰产、优质、抗盐碱、耐冷凉、抗病、抗倒伏、早熟于一体的多优集成新品种绥粳 5号。

多优型腹膜透析管和弹簧式腹膜透析管毛刷的研制

为了减少腹膜透析的并发症，提高其疗效，在中国医学科学院检索了10余年近400万篇文献和有关腹膜透析管的图纸的基础上结合笔者的临床经验，研制了多优型腹膜透析管和弹簧式腹膜透析管毛刷。

结构形态与空间动态视觉力的互优营造——以悬垂和反垂结构界面为例

以基于重力规律生成的悬垂和反垂结构为研究对象,梳理其发展脉络,结合案例分析设计方法和空间表现,展示思维创新和自然规律的结合能达成的结构效能和空间视感的表现。空间动态的视觉力不仅是结构形态的被动结果,更是触发更优结构和更美秩序的媒介,并与结构形态积极互动,从而优化最终的空间设计结果。

优质高产抗病水稻品种松粳6号特性分析

通过对松粳6号亲本的系谱分析,米质分析,产量鉴定、全省区域试验和生产试验的产量结果分析,以及抗稻瘟病性分析表明,松粳6号具有优质、高产、抗稻瘟病性强等特性,是水稻育种的重要种质资源,是目前水稻生产中多优集成的水稻新品种。依据稻瘟病发病机理提出松粳6号要同更多的具有其他抗稻瘟病基因的品种搭配应用,使其具有更长的应用时期。

New immune multiobjective optimization algorithm and its application in boiler combustion optimization

In order to meet the requirements of combustion optimization for saving energy and reducing pollutant emission simultaneously,an immune cell subsets based multiobjective optimization algorithm（ICSMOA）is proposed.In the ICSMOA,the subset division operator and the immunological tolerance operation are defined.Preference can be easily addressed by using the subset division operator,and the distribution of the solutions can be guaranteed by the immunological tolerance operation.Using the ICSMOA,a group of Pareto optimal solutions can be obtained.However,by the traditional weighting method（WM）,only one solution can be obtained and it cannot be judged as Pareto optimal or not.In contrast to the solutions obtained by the repeatedly performed WM,the simulation results show that most solutions obtained by the ICSMOA are better than the solutions obtained by the WM.In addition,the Pareto front obtained by the ICSMOA is not as uniform as most classical multiobjective optimization algorithms.More optimal solutions which meet the preference set by the decision-maker can be obtained and they are very useful for industrial application.

Multi-objective integrated optimization based on evolutionary strategy with a dynamic weighting schedule

The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system performance and control cost are defined by H2 or H∞ norms. During this optimization process, the weights are varying with the increasing generation instead of fixed values. The proposed strategy together with the linear matrix inequality （LMI） or the Riccati controller design method can find a series of uniformly distributed nondominated solutions in a single run. Therefore, this method can greatly reduce the computation intensity of the integrated optimization problem compared with the weight-based single objective genetic algorithm. Active automotive suspension is adopted as an example to illustrate the effectiveness of the proposed method.

Static and dynamic collaborative optimization of ship hull structure

The goal of this effort was to provide a static and dynamic collaborative optimization （CO） model for the design of ship hull structure. The CO model integrated with static, mode and dynamic analyses. In the system-level optimization model, a new objective function was advised, integrating all the subsystem-levels＇ objective functions, so as to eliminate the effects of dimensions and magnitude order. The proposed CO architecture enabled multi-objectives of the system and subsystem-level to be considered at both levels during optimization. A bi-level optimization strategy was advised, using the multi-island genetic algorithm. The proposed model was demonstrated with a deck optimization problem of container ship stern. The analysis progress and results of example show that the CO strategy is not only feasible and reliable, but also well suited for use in actual optimization problems of ship design.

SCRAMJET INLET MULTI-OBJECTIVE OPTIMIZATION BASED ON RESPONSE SURFACE METHODOLOGY

The uniform design and response surface methodology （RSM） are applied to the multi-objective optimization of a 2-D mixed compression scramjet inlet. The set of experimental design points on the design space is selected by the uniform design, and the inlet performance is analyzed by computational fluid dynamics （CFD）. Then complete quadratic polynomial response surface approximation models are constructed based on the performance analysis results and then used to replace theoriginal complex inlet performance model. The optimization is conducted using a multi-objective genetic algorithm NSGA-Ⅱ, and the Pareto optimal solution set is obtained. Results show that the uniform design and RSM can reduce the computational complexity of numerical simulation and improve the optimization efficiency.

MULTI-OBJECTIVE SHAPE DESIGN IN AERODYNAMICS USING GAME STRATEGY

Multi-objective optimization for the optimum shape design is introduced in aerodynamics using the Game theory. Based on the control theory, the employed optimizer and the negative feedback are used to implement the constraints. All the constraints are satisfied implicitly and automatically in the design. Furthermore,the above methodology is combined with a formulation derived from the Game theory to treat multi-point airfoil optimization. Airfoil shapes are optimized according to various aerodynamics criteria. In the symmetric Nash game, each “player” is responsible for one criterion, and the Nash equilibrium provides a solution to the multipoint optimization. Design results confirm the efficiency of the method.

EFFICIENT METHOD FOR MULTIDISCIPLINARY DESIGN OPTIMIZATION BY CONSIDERING UNCERTAINTY

A new reliability-based multidisciplinary design optimization （RBMDO） framework is proposed by combining the single-loop-based reliability analysis （SLBRA） method with multidisciplinary feasible （MDF） method. The Kriging approximate model with updating is introduced to reduce the computational cost of MDF caused by the complex structure. The computational efficiency is remarkably improved as the lack of iterative process during reliability analysis. Special attention is paid to a turbine blade design optimization by adopting the proposed method. Results show that the method is much more efficient than the commonly used double-loop based RBMDO method. It is feasible and efficient to apply the method to the engineering design.

HYBRID MULTI-OBJECTIVE GRADIENT ALGORITHM FOR INVERSE PLANNING OF IMRT

The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to analyze the efficiency of the algorithm. In the simulation case of the water phantom, the algorithm is applied to an inverse planning process of intensity modulated radiation treatment （IMRT）. The objective functions of planning target volume （PTV） and normal tissue （NT） are based on the average dose distribution. The obtained intensity profile shows that the hybrid multi-objective gradient algorithm saves the computational time and has good accuracy, thus meeting the requirements of practical applications.

MULTIOBJECTIVE OPTIMIZATION OF EIGHT-DOF VEHICLE SUSPENSION BASED ON GAME THEORY

A systematic and effective optimization is proposed for the design of a three-dimensional （3-D） vehicle suspension model with eight degrees of freedom （DOF）, including vertical seat motion, vehicle suspension, pitching and rolling motions, and vertical wheel motions using the evolutionary game theory. A new design of the passive suspension is aided by game theory to attain the best compromise between ride quality and suspension deflections. Extensive simulations are performed on three type road surface models A, B, C pavement grades based on the guidelines provided by ISO-2631 with the Matlab/Simulink environment. The preliminary results show that, when the passive suspension is optimized via the proposed approach, a substantial improvement in the vertical ride quality is obtained while keeping the suspension deflections within their allowable clearance when the vehicle moves at a constant velocity v=20 m/s, and the comfort performance of a suspension seat can be enhanced by 20%-30%.

IMPROVED AERODYNAMIC APPROXIMATION MODEL WITH CASE-BASED REASONING TECHNIQUE FOR MDO OF AIRCRAFT

To increase the efficiency of the multidisciplinary optimization of aircraft, an aerodynamic approximation model is improved. Based on the study of aerodynamic approximation model constructed by the scaling correction model, case-based reasoning technique is introduced to improve the approximation model for optimization. The aircraft case model is constructed by utilizing the plane parameters related to aerodynamic characteristics as attributes of cases, and the formula of case retrieving is improved. Finally, the aerodynamic approximation model for optimization is improved by reusing the correction factors of the most similar aircraft to the current one. The multidisciplinary optimization of a civil aircraft concept is carried out with the improved aerodynamic approximation model. The results demonstrate that the precision and the efficiency of the optimization can be improved by utilizing the improved aerodynamic approximation model with ease-based reasoning technique.

Phase-field simulation of forced flow effect on random preferred growth direction of multiple grains

The random distribution problem of dendrite preferred growth direction was settled by random grid method.This method was used to study the influence of forced laminar flow effect on multiple grains during solidification.Taking high pure succinonitrile （SCN） undercooled melt as an example,the forced laminar flow effect on multiple grains was studied by phase-field model of single grain which coupled with flow equations at non-isothermal condition.The simulation results show that the random grid method can reasonably settle the problem of random distribution and is more effective.When the solid fraction is relatively low,melt particles flow around the downstream side of dendrite,and the flow velocity between two dendrite arms becomes high.At the stage of solidification time less than 1800Δt,every dendrite grows freely;the upstream dendrites are stronger than the downstream ones.The higher the melt flow rate,the higher the solid fraction.However,when the solid fraction is relatively high,the dendrite arm intertwins and only a little residual melt which is not encapsulated can flow;the solid fraction will gradually tend to equal to solid fraction of melt without flow.

INTEGRATED DESIGN OF AN ELECTRIC POWERED UNMANNED AIR VEHICLE USING CONCURRENT SUBSPACE DESIGN

The concurrent subspace design (CSD) framework has been used to conduct a preliminary design optimization of an electric powered, unmanned air vehicle (EPUAV) operating at a low Reynolds number. A multidisciplinary system analysis that includes aerodynamics, weights, propulsion, performance and stability and control has been developed for this class of vehicles. The CSD framework employs artificial neural network based response surfaces to provide approximations to the design space. The EPUAV system includes 25 continuous and 4 discrete design variables. The CSD framework was able to identify feasible designs with significant weight reductions relative to any previously considered (i.e. initial database) designs. This was accomplished with a limited number of system analyses. The results also demonstrate the nature of this design framework adaptive to changes in design requirements.

HEURISTIC PARTICLE SWARM OPTIMIZATION ALGORITHM FOR AIR COMBAT DECISION-MAKING ON CMTA

Combining the heuristic algorithm （HA） developed based on the specific knowledge of the cooperative multiple target attack （CMTA） tactics and the particle swarm optimization （PSO）, a heuristic particle swarm optimization （HPSO） algorithm is proposed to solve the decision-making （DM） problem. HA facilitates to search the local optimum in the neighborhood of a solution, while the PSO algorithm tends to explore the search space for possible solutions. Combining the advantages of HA and PSO, HPSO algorithms can find out the global optimum quickly and efficiently. It obtains the DM solution by seeking for the optimal assignment of missiles of friendly fighter aircrafts （FAs） to hostile FAs. Simulation results show that the proposed algorithm is superior to the general PSO algorithm and two GA based algorithms in searching for the best solution to the DM problem.