网络最大流Pareto扩充研究

将网络容量定义为最大s-t流的流量,建立了带有时间和费用双重限制下的网络容量扩充问题模型。通过网络变换,将该问题转化为可利用成熟算法求解的线性最小费用流问题。研究了给定网络容量扩充目标要求下,求解所有关于时间和费用的Pareto优化解问题并提供了相应算法。研究内容不仅适用于各种情形的容量扩充问题,而且还可应用于网络规划。最后通过具体例子的求解,说明了算法的正确性和有效性。

低温柔性管道关键结构层的多目标优化设计

低温柔性管道是海上浮式液化天然气装置系统(FLNG)的核心输运装备。针对低温柔性管道多材料、多层复合的结构设计难点,将其按照不同功能解耦成内衬层、抗拉铠装层以及辅助层三个关键结构层。基于神经网络模型(RBF)、Kriging模型以及响应面模型(RSM)三种建模方法建立了上述三个结构层响应分析的代理模型,并通过模型准确度的比较,发现RBF的误差均最小。在优化设计中,基于遗传算法分别对低温柔性管道上述关键结构层进行多目标优化设计。在内衬层结构的优化中,以质量及弯曲刚度最小为优化目标;在抗拉铠装层结构的优化中,以质量最小及拉伸刚度最大为优化目标;在辅助层结构的优化中,以质量、弯曲刚度及传热速率最小为优化目标。研究工作为低温柔性管道的结构提供了关键设计参数及理性的设计方法。

On ε-Constraint Based Methods for the Generation of Pareto Frontiers

Over the years, a number of methods have been proposed for the generation of uniform and globally optimal Pareto frontiers in multi-objective optimization problems. This has been the case irrespective of the problem definition. The most commonly applied methods are the normal constraint method and the normal boundary intersection method. The former suffers from the deficiency of an uneven Pareto set distribution in the case of vertical （or horizontal） sections in the Pareto frontier, whereas the latter suffers from a sparsely populated Pareto frontier when the optimization problem is numerically demanding （ill-conditioned）. The method proposed in this paper, coupled with a simple Pareto filter, addresses these two deficiencies to generate a uniform, globally optimal, well-populated Pareto frontier for any feasible bi-objective optimization problem. A number of examples are provided to demonstrate the performance of the algorithm.

基于多目标进化算法的统一潮流控制器稳定控制器鲁棒运行点选择

针对多机电力系统中UPFC稳定控制器鲁棒运行点的选择问题,提出了综合考虑UPFC装置向系统提供阻尼以及受控电力系统本身阻尼两个目标的选择方法,将该问题转化为一个多目标问题,并提出求解该问题的相应多目标进化算法(MOEA),该算法基于Pareto排序选择方法,采取模糊修正、小生境算子、精英选种策略等技术,具有收敛速度快等优点。最后通过对新英格兰电力系统的仿真分析,验证了多目标进化方法在选取UPFC稳定控制器鲁棒运行点问题上的有效性。

带有时间和费用限制的网络容量扩充问题

研究了带有时间和费用双重限制下的网络容光焕发量扩充问题，即在满足时间和费用约束条件下，如何扩充网络中某些弧的容量使扩充后的网络容量尽可能的大。通过将问题转化为 求其最小生成树，给出了其求解算法。由于在扩充时两个约束条件一般有一者剩余，为了合 理配置扩充资源，讨论了给定容量要求下关于时间和费用的Pareto优化解的情况并给出了有 效算法，用实例来证明了算法的有效性。

具有局部和全局QoS约束的Web服务选择

提出一种具有局部和全局QoS约束的Web服务选择算法。将基于QoS的Web服务选择问题建模为带QoS约束的多目标组合优化问题,使用局部QoS约束过滤不满足约束条件的候选服务,通过归档式多目标模拟退火(AMOSA)算法同时优化多个QoS目标函数以产生一组Pareto优化解,利用全局QoS约束筛选出满足约束条件的Pareto最优解集。实验结果证明了该算法的有效性。

A Fuzzy Satisfactory Method for Decentralized Bi Level Linear Programming

Based on the theory of fuzzy decision making, a two phrase approach is proposed for the decentralized bi level linear programming problem(DBLPP). The approach considers the conflicts between the upper and lower levels decision makers (DMs), and among the lower level DMs themselves, a satisfactory solution is got with the non conflict matrix and decision power distribution. Compared with the other methods that have ever been proposed, the solution process is more fit to a kind of real decision making processes.

基于执行时段滑动调整策略的中继卫星任务规划算法设计

针对中继卫星任务规划问题开展研究,提出一种基于执行时段滑动调整策略的任务规划算法。首先分析了中继卫星资源调度过程,并对任务申请的时间特征进行形式化描述,然后归纳出任务分配的主要约束,从而完成问题建模。在此基础上,对优化算法进行设计,给出了执行时段滑动调整步骤。通过调整已规划任务执行时段的方法,使部分原本无法执行任务具备执行可能,提升了任务执行率和资源利用率。在仿真实验中,通过大规模测试分析对比不同算法的优化效果,验证了文中所提方法的有效性。

Multi-objective optimization for draft scheduling of hot strip mill

A multi-objective optimization model for draft scheduling of hot strip mill was presented, rolling power minimizing, rolling force ratio distribution and good strip shape as the objective functions. A multi-objective differential evolution algorithm based on decomposition （MODE/D）. The two-objective and three-objective optimization experiments were performed respectively to demonstrate the optimal solutions of trade-off. The simulation results show that MODE/D can obtain a good Pareto-optimal front, which suggests a series of alternative solutions to draft scheduling. The extreme Pareto solutions are found feasible and the centres of the Pareto fronts give a good compromise. The conflict exists between each two ones of three objectives. The final optimal solution is selected from the Pareto-optimal front by the importance of objectives, and it can achieve a better performance in all objective dimensions than the empirical solutions. Finally, the practical application cases confirm the feasibility of the multi-objective approach, and the optimal solutions can gain a better rolling stability than the empirical solutions, and strip flatness decreases from （0± 63） IU to （0±45） IU in industrial production.

Layout problem of multi-component systems arising for improving maintainability

To improve the mainlainability design efficiency and quality, a layout optimization method for maintainability of multi-component systems was proposed. The impact of the component layout design on system maintainability was analyzed, and the layout problem for maintainability was presented. It was formulated as an optimization problem, where maintainability, layout space and distance requirement were formulated as objective functions. A multi-objective particle swarm optimization algorithm, in which the constrained-domination relationship and the update strategy of the global best were simply modified, was then used to obtain Pareto optimal solutions for the maintainability layout design problem. Finally, application in oxygen generation system of a spacecraft was studied in detail to illustrate the effectiveness and usefulness of the proposed method. The results show that the concurrent maintainability design can be carried out during the layout design process by solving the layout optimization problem for maintainability.

Optimal design of dynamic and control performance for planar manipulator

A design and optimization approach of dynamic and control performance for a two-DOF planar manipulator was proposed.After the kinematic and dynamic analysis,several advantages of the mechanism were illustrated,which made it possible to obtain good dynamic and control performances just through mechanism optimization.Based on the idea of design for control(DFC),a novel kind of multi-objective optimization model was proposed.There were three optimization objectives:the index of inertia,the index describing the dynamic coupling effects and the global condition number.Other indexes to characterize the designing requirements such as the velocity of end-effector,the workspace size,and the first mode natural frequency were regarded as the constraints.The cross-section area and length of the linkages were chosen as the design variables.NSGA-II algorithm was introduced to solve this complex multi-objective optimization problem.Additional criteria from engineering experience were incorporated into the selecting of final parameters among the obtained Pareto solution sets.Finally,experiments were performed to validate the linear dynamic structure and control performances of the optimized mechanisms.A new expression for measuring the dynamic coupling degree with clear physical meaning was proposed.The results show that the optimized mechanism has an approximate decoupled dynamics structure,and each active joint can be regarded as a linear SISO system.The control performances of the linear and nonlinear controllers were also compared.It can be concluded that the optimized mechanism can achieve good control performance only using a linear controller.

A Parameter Determination Method of Distribution Voltage Regulators Considering Tap Change and Voltage Profile

This paper proposes a parameter determination method of distribution voltage regulators load ratio control transformers （LRT） and step voltage regulators （SVR） considering the tap change and voltage profile. The method takes two procedures in order to simplify the optimization problem and to reduce calculation time. One is to simultaneously determine the control parameters of LRT and SVR minimizing voltage violations and voltage variations. The algorithm is based on particle swarm optimization （PSO）, which is one of non-linear optimization methods by using a concept of swarm intelligence. Another is to determine the dead-band width of LRT and SVR on the basis of bi-evaluation of tap change and voltage margin. The concept of a Pareto optimal solution is used for the decision of the best dead-band width. As the results of numerical simulations using distribution network model, the validity of the proposed method has been affirmed.

Robust optimization of nonlinear impulsive rendezvous with uncertainty

The optimal rendezvous trajectory designs in many current research efforts do not incorporate the practical uncertainties into the closed loop of the design.A robust optimization design method for a nonlinear rendezvous trajectory with uncertainty is proposed in this paper.One performance index related to the variances of the terminal state error is termed the robustness performance index,and a two-objective optimization model(including the minimum characteristic velocity and the minimum robustness performance index)is formulated on the basis of the Lambert algorithm.A multi-objective,non-dominated sorting genetic algorithm is employed to obtain the Pareto optimal solution set.It is shown that the proposed approach can be used to quickly obtain several inherent principles of the rendezvous trajectory by taking practical errors into account.Furthermore,this approach can identify the most preferable design space in which a specific solution for the actual application of the rendezvous control should be chosen.

Modeling and multi-objective optimization of a gasoline engine using neural networks and evolutionary algorithms

In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （SI） gasoline engine. The aim of this optimization is to reduce engine emissions in terms of carbon monoxide （CO）, hydrocarbons （HC）, and nitrogen oxides （NOx）, which are the causes of diverse environmental problems such as air pollution and global warming. Stationary engine tests were performed for data generation, covering 60 operating conditions. Artificial neural networks （ANNs） were used to predict exhaust emissions, whose inputs were from six engine operating parameters, and the outputs were three resulting exhaust emissions. The outputs of ANNs were used to evaluate objective functions within the optimization algorithms： NSGA-II and MOPSO. Then a decision-making process was conducted, using a fuzzy method to select a Pareto solution with which the best emission reductions can be achieved. The NSGA-II algorithm achieved reductions of at least 9.84%, 82.44%, and 13.78% for CO, HC, and NOx, respectively. With a MOPSO algorithm the reached reductions were at least 13.68%, 83.80%, and 7.67% for CO, HC, and NOx, respectively.

Multi-objective optimization for deepwater dynamic umbilical installation analysis

We suggest a method of multi-objective optimization based on approximation model for dynamic umbilical installation. The optimization aims to find out the most cost effective size, quantity and location of buoyancy modules for umbilical installation while maintaining structural safety. The approximation model is constructed by the design of experiment (DOE) sampling and is utilized to solve the problem of time-consuming analyses. The non-linear dynamic analyses considering environmental loadings are executed on these sample points from DOE. Non-dominated Sorting Genetic Algorithm (NSGA-II) is employed to obtain the Pareto solution set through an evolutionary optimization process. Intuitionist fuzzy set theory is applied for selecting the best compromise solution from Pareto set. The optimization results indicate this optimization strategy with approximation model and multiple attribute decision-making method is valid, and provide the optimal deployment method for deepwater dynamic umbilical buoyancy modules.