基于约束性多目标优化算法的柑橘黄龙病识别算法

为了克服柑橘黄龙病监测和识别难的问题,提出一种基于约束性多目标优化算法的柑橘黄龙病识别算法(CMOA-CHDIA)。将柑橘黄龙病特征最优化转换为约束性多目标优化问题模型,采用基于勒贝格测度约束性多目标粒子群优化算法(LCMOPSO)求解该模型,并获取最优柑橘黄龙病特征;根据获得的最优特征采用近邻分类算法识别柑橘黄龙病。试验结果显示,基于约束性多目标优化算法的柑橘黄龙病识别算法对柑橘黄龙病的识别准确率达到98.55%,较FSMEA、PSO-MOFSA、FSMDEA识别准确率分别增加14.55%、11.55%、12.55%,在提高柑橘黄龙病识别准确度方面效果较好,对柑橘黄龙病防治具有一定的指导意义。

遗传算法在汽车动力传动系参数多目标优化中的应用

针对汽车的动力性和燃油经济性，提出了汽车动力传动系参数的多目标优化设计的数学模型，在设计过程中，应用了一种高效的寻优算法-遗传算法，并给出了计算实例，结果表明，该方法可以获得令人满意的效果。

基于遗传算法的混合流水线车间调度多目标求解

为了解决传统的多目标优化算法难以很好实现企业的实际决策需要问题,针对混合流水线车间调度(HFSP)的多目标优化调度问题,提出了一种新的多目标遗传算法。根据企业实际需求,采用分模块两层建模的思想,将多目标分为约束性目标和优化性目标。算法根据目标性质的不同分别进行不同的搜索。最后将新算法应用于HFSP多目标优化问题进行实例验证。结果表明,所提出的算法具有很好的可行性,与其他多目标优化方法相比,该算法具有明显的优越性、实用性和可操作性。

Stable MIMO Constrained Predictive Control with Steadystate Objective Optimization

A two-stage multi-objective optimization model-predictive control algorithms(MPC) strategy is presented. A domain MPC controller with input constraints is used to increase freedom for steady-state objective and enhance stabilization of the controller. A steady-state objective optimization algorithm oriented to transient process is adopted to realize optimization of objectives else than dynamic control. It is proved that the stabilization for both dynamic control and steady-state objective optimization can be guaranteed. The theoretical results are demonstrated and discussed using a distillation tower as the model. Theoretical analysis and simulation results show that this control strategy is efficient and provides a good strategic solution to practical process control.

Composite multiobjective optimization beamforming based on genetic algorithms

All the parameters of beamforming are usually optimized simultaneously in implementing the optimization of antenna array pattern with multiple objectives and parameters by genetic algorithms （GAs）. Firstly, this paper analyzes the performance of fitness functions of previous algorithms. It shows that original algorithms make the fitness functions too complex leading to large amount of calculation, and also the selection of the weight of parameters very sensitive due to many parameters optimized simultaneously. This paper proposes a kind of algorithm of composite beamforming, which detaches the antenna array into two parts corresponding to optimization of different objective parameters respectively. New algorithm substitutes the previous complex fitness function with two simpler functions. Both theoretical analysis and simulation results show that this method simplifies the selection of weighting parameters and reduces the complexity of calculation. Furthermore, the algorithm has better performance in lowering side lobe and interferences in comparison with conventional algorithms of beamforming in the case of slightly widening the main lobe.

Performance-based passive control analysis of adjacent structures:Optimization of Maxwell dampers

The performance-based passive control analysis of the Maxwell dampers between one 10-story and one 6-story adjacent RC frames is conducted in this work.Not only the optimal parameters but also the optimal arrangements of the Maxwell dampers are proposed based on the optimal target of making the total exceeding probability of the adjacent structures to be minimal.The applicability of the analytical expressions of the Maxwell damper damping parameters under different seismic performance targets are firstly examined and then the preferable damping parameters of the Maxwell dampers are proposed through the extensive parametric studies.Furthermore,the optimal arranging positions and optimal arranging numbers of the Maxwell dampers between the adjacent buildings are derived based on a large number of seismic fragility analyses,as well.The general arranging laws of the Maxwell dampers between the adjacent buildings are generated based on the discussion of the theoretical method through the simplified plane model.The optimal parameters and optimal arrangement of the Maxwell dampers presented make both the adjacent structures have preferable controlled effects under each seismic performance target which can satisfy the requirements of multi-performance seismic resistance of the modern seismic codes.

Satellite Constellation Design with Multi-Objective Genetic Algorithm for Regional Terrestrial Satellite Network

Constellations design for regional terrestrial-satellite network can strengthen the coverage for incomplete terrestrial cellular network. In this paper, a regional satellite constellation design scheme with multiple feature points and multiple optimization indicators is proposed by comprehensively considering multi-objective optimization and genetic algorithm, and "the Belt and Road" model is presented in the way of dividing over 70 nations into three regular target areas. Following this, we formulate the optimization model and devise a multi-objective genetic algorithm suited for the regional area with the coverage rate under simulating, computing and determining. Meanwhile, the total number of satellites in the constellation is reduced by calculating the ratio of actual coverage of a single-orbit constellation and the area of targets. Moreover, the constellations' performances of the proposed scheme are investigated with the connection of C++ and Satellite Tool Kit(STK). Simulation results show that the designed satellite constellations can achieve a good coverage of the target areas.

Modeling and Multi-objective Optimization of Refinery Hydrogen Network

The demand of hydrogen in oil refinery is increasing as market forces and environmental legislation, so hydrogen network management is becoming increasingly important in refineries. Most studies focused on single-objective optimization problem for the hydrogen network, but few account for the multi-objective optimization problem. This paper presents a novel approach for modeling and multi-objective optimization for hydrogen network in refineries. An improved multi-objective optimization model is proposed based on the concept of superstructure. The optimization includes minimization of operating cost and minimization of investment cost of equipment. The proposed methodology for the multi-objective optimization of hydrogen network takes into account flow rate constraints, pressure constraints, purity constraints, impurity constraints, payback period, etc. The method considers all the feasible connections and subjects this to mixed-integer nonlinear programming （MINLP）. A deterministic optimization method is applied to solve this multi-objective optimization problem. Finally, a real case study is intro-duced to illustrate the applicability of the approach.

Model of Optimal Forest Management Unit Area for a Sustainable Forest Resource Administration

Deforestation is one of the most crucial problems in Indonesia. It creates a number of environmental problems, e.g., landslides, loss of biological diversity, and decrease of carbon sequestration which are impacting human lives, There are a number of underlying causes, often stemmed from the emphasis on a particular forest management objective. Despite numerous forest policies and regulations established by the government, deforestation continues to happen. Quite often, the policies and regulations remain ＂paper tiger＂, which means ineffectual on the ground. One of the contributing factors to deforestation is the lack of custodian for the management of the forests. The regulation, such as law No. 41/1999, stipulates that all state forests must be managed under a FMI3 （forest management unit） as the custodians. The objective of this research is to develop an optimal model for FMU. Using the LGP （linear goal programming）, this research attempts to develop an optimum model for conservation areas. It assumes that the existing zoning approach implemented by the government in conservation areas is inappropriate. The optimum zoning model is expected to improve forest management and restore the forest function as a life supporting system and biological diversity conservation.

Optimization of air quantity regulation in mine ventilation networks using the improved differential evolution algorithm and critical path method

In mine ventilation networks, the reasonable airflow distribution is very important for the production safety and economy. Three basic problems of the natural, full-controlled and semi-controlled splitting were reviewed in the paper. Aiming at the high difficulty semi-controlled splitting problem, the general nonlinear multi-objectives optimization mathematical model with constraints was established based on the theory of mine ventilation networks. A new algorithm, which combined the improved differential evaluation and the critical path method （CPM） based on the multivariable separate solution strategy, was put forward to search for the global optimal solution more efficiently. In each step of evolution, the feasible solutions of air quantity distribution are firstly produced by the improved differential evolu- tion algorithm, and then the optimal solutions of regulator pressure drop are obtained by the CPM. Through finite steps iterations, the optimal solution can be given. In this new algorithm, the population of feasible solutions were sorted and grouped for enhancing the global search ability and the individuals in general group were randomly initialized for keeping diversity. Meanwhile, the individual neighbor- hood in the fine group which may be closely to the optimal solutions were searched locally and slightly for achieving a balance between global searching and local searching, thus improving the convergence rate. The computer program was developed based on this method. Finally, the two ventilation networks with single-fan and multi-fans were solved. The results show that this algorithm has advantages of high effectiveness, fast convergence, good robustness and flexibility. This computer program could be used to solve lar^e-scale ~eneralized ventilation networks o^timization problem in the future.

Reliability-based robust multi-obj ective optimization of a 5-DOF vehicle vibration model subjected to random road profiles

Ride and handling are two paramount factors in design and development of vehicle suspension systems. Conflicting trends in ride and handling characteristics propel engineers toward employing multi-objective optimization methods capable of providing the best trade-off designs compromising both criteria simultaneously. Although many studies have been performed on multi-objective optimization of vehicle suspension system, only a few of them have used probabilistic approaches considering effects of uncertainties in the design. However, it has been proved that optimum point obtained from deterministic optimization without taking into account the effects of uncertainties may lead to high-risk points instead of optimum ones. In this work, reliability-based robust multi-objective optimization of a 5 degree of freedom （5-DOF） vehicle suspension system is performed using method of non-dominated sorting genetic algorithm-II （NSGA-II） in conjunction with Monte Carlo simulation （MCS） to obtain best designs considering both comfort and handling. Road profile is modeled as a random function using power spectral density （PSD） which is in better accordance with reality. To accommodate the robust approach, the variance of all objective functions is also considered to be minimized. Also, to take into account the reliability criterion, a reliability-based constraint is considered in the optimization. A deterministic optimization has also been performed to compare the results with probabilistic study and some other deterministic studies in the literature. In addition, sensitivity analysis has been performed to reveal the effects of different design variables on objective functions. To introduce the best trade-off points from the obtained Pareto fronts, TOPSIS method has been employed. Results show that optimum design point obtained from probabilistic optimization in this work provides better performance while demonstrating very good reliability and robustness. However, other optimum points from deterministic optimizations violate the regarded constraints in the presence of uncertainties.

Weigh in Motion Based on Parameters Optimization

Dynamic tire forces are the main factor affecting the measurement accuracy of the axle weight of moving vehicle.This paper presents a novel method to reduce the influence of the dynamic tire forces on the weighing accuracy.On the basis of analyzing the characteristic of the dynamic tire forces,the objective optimization equation is constructed.The optimization algorithm is presented to get the optimal estimations of the objective parameters.According to the estimations of the parameters,the dynamic tire forces are separated from the axle weigh signal.The results of simulation and field experiments prove the effectiveness of the proposed method.

Investigation on Multi-objective Performance Optimization Algorithm Application of Fan Based on Response Surface Method and Entropy Method

A multi-objective performance optimization method is proposed, and the problem that single structural parame- ters of small fan balance the optimization between the static characteristics and the aerodynamic noise is solved. In this method, three structural parameters are selected as the optimization variables. Besides, the static pressure efficiency and the aerodynamic noise of the fan are regarded as the multi-objective performance. Furthermore, the response surface method and the entropy method are used to establish the optimization function between the op- timization variables and the multi-objective performances. Finally, the optimized model is found when the opti- mization function reaches its maximttm value. Experimental data shows that the optimized model not only en- hances the static characteristics of the fan but also obviously reduces the noise. The results of the study will provide some reference for the optimization of multi-objective performance of other types of rotating machinery.

Optimal Land Use Structure for Sustainable Agricultural Development——A Case Study in Changsha County,South Central China

Environmental and social problems caused by overfertilization,excessive pesticides,and encroachment on farmland are increasingly serious in agricultural settings,especially in suburban agricultural areas and highly intensive agricultural areas.Hence,modern agriculture not only pursues economic benefits,but it also pays more attention to ecological functions and social stability.This paper proposes a set of methods which are designed to realize optimal agricultural benefits and sustainable development by scientifically adjusting the land use structure.Taking Changsha County in South Central China as a case study,this paper first built an index system and adopted the information entropy-TOPSIS method to assess the economic,social,and ecological benefits of agricultural land use.Next,a coupled coordination model and an obstacle model were chosen to diagnose those factors that remained as obstacles to achieving the sustainable and coordinated development of the benefits of agricultural land use.Finally,based on the analysis of the changes in the benefits and obstacles over time,socio-economic and ecological constraints were established,and the multi-objective linear programming method(MOLP)was used to determine the comprehensive benefits and optimal land use structure.The results indicate that:(1)The agricultural benefits were stably increasing from 0.20 in 1996 to 0.79 in 2016.(2)The economic benefit index is no longer the main obstacle,while the social benefit index,which includes components such as the food security index,has become the principal influencing factor.(3)The optimal land use structure and comprehensive benefits were presented by taking into consideration the economic development,environmental protection,and social needs.This study emphasizes economic development,but it also seeks coordinated development with comprehensive benefits.The results of the study could provide scientific recommendations for optimizing the agricultural land use spatial patterns and sustainable land use.

Generatrix shape optimization of stiffened shells for low imperfection sensitivity

According to previous studies,stiffened shells with convex hyperbolic generatrix shape are less sensitive to imperfections.In this study,the effects of generatrix shape on the performances of elastic and plastic buckling in stiffened shells are investigated.Then,a more general description of generatrix shape is proposed,which can simply be expressed as a convex B-spline curve(controlled by four key points).An optimization framework of stiffened shells with a convex B-spline generatrix is established,with optimization objective being measured in terms of nominal collapse load,which can be expressed as a weighted sum of geometrically imperfect shells.The effectiveness of the proposed framework is demonstrated by a detailed comparison of the optimum designs for the B-spline and hyperbolic generatrix shapes.The decrease of imperfection sensitivity allows for a significant weight saving,which is particularly important in the development of future heavy-lift launch vehicles.

Flow reconstructions and aerodynamic shape optimization of turbomachinery blades by POD-based hybrid models

This study presented a hybrid model method based on proper orthogonal decomposition(POD) for flow field reconstructions and aerodynamic design optimization. The POD basis modes have better description performance in a system space compared to the widely used semi-empirical basis functions because they are obtained through singular value decomposition of the system.Instead of the widely used linear regression, nonlinear regression methods are used in the function response of the coefficients of POD basis modes. Moreover, an adaptive Latin hypercube design method with improved space filling and correlation based on a multi-objective optimization approach was employed to supply the necessary samples. Prior to design optimization, the response performance of POD-based hybrid models was first investigated and validated through flow reconstructions of both single-and multiple blade rows. Then, an inverse design was performed to approach a given spanwise flow turning distribution at the outlet of a turbine blade by changing the spanwise stagger angle, based on the hybrid model method. Finally, the span wise blade sweep of a transonic compressor rotor and the spanwise stagger angle of the stator blade of a single low-speed compressor stage were modified to reduce the flow losses with the constraints of mass flow rate, total pressure ratio, and outlet flow turning.The results are presented in detail, demonstrating the good response performance of POD-based hybrid models on missing data reconstructions and the effectiveness of POD-based hybrid model method in aerodynamic design optimization.

Differences and relations of objectives, constraints, and decision parameters in the optimization of individual heat exchangers and thermal systems

Performance improvement of heat exchangers and the corresponding thermal systems benefits energy conservation, which is a multi-parameters, multi-objectives and multi-levels optimization problem. However, the optimized results of heat exchangers with improper decision parameters or objectives do not contribute and even against thermal system performance improvement. After deducing the inherent overall relations between the decision parameters and designing requirements for a typical heat exchanger network and by applying the Lagrange multiplier method, several different optimization equation sets are derived, the solutions of which offer the optimal decision parameters corresponding to different specific optimization objectives, respectively. Comparison of the optimized results clarifies that it should take the whole system, rather than individual heat exchangers, into account to optimize the fluid heat capacity rates and the heat transfer areas to minimize the total heat transfer area, the total heat capacity rate or the total entropy generation rate, while increasing the heat transfer coefficients of individual heat exchangers with different given heat capacity rates benefits the system performance. Besides, different objectives result in different optimization results due to their different intentions, and thus the optimization objectives should be chosen reasonably based on practical applications, where the inherent overall physical constraints of decision parameters are necessary and essential to be built in advance.

Enhancement of Performance by Blade Optimization in Two-Stage Ring Blower

This paper describes the shape optimization of an impeller used for two-stage high pressure ring blower.Two shape variables,which are used to define an impeller shape,are introduced to increase the blower performance.The pressure of a blower is selected as an object function,and the blade optimization is performed by a response surface method.Three-dimensional Navier-Stokes equations are introduced to analyze the internal flow of the blower and to find the value of object function for the training data.Relatively good agreement between experimental measurements and numerical simulation is obtained in the present study.Throughout the shape optimization,it is found that a hub height is effective to increase pressure in the ring blower.The pressure rise for the optimal two-stage ring blower is successfully increased up to 1.86% compared with that of reference at the design flow rate.Local recirculation flow having low velocity is formed in both sides of the impeller outlet by different flow direction of the inlet and outlet of the impeller.Detailed flow field inside the ring blower is also analyzed and discussed.