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1.
An application to structural design of an innovative method for optimising stochastic systems is introduced in the paper. The proposed method allows one to carry out both the multi-objective optimisation of a structural element and to improve the robustness of the design. The innovative method is rather general. To show its effectiveness, an ideal cantilever has been designed in order to minimise both mass and deflection. The cantilever is shaped as a beam and is subject to random loads acting at its free end. The beam geometrical dimensions and material properties vary stochastically due to manufacturing tolerances. Different beam cross sections and two different materials (aluminium alloy and steel) have been considered. From the optimisation, it turned out that the optimal solutions are the O and the I beam, depending on the required lightness and stiffness. Compared to steel, aluminium alloy beams have provided better (or at least equal) performance.  相似文献   
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In this paper, the modelling and multi-objective optimal control of batch processes, using a recurrent neuro-fuzzy network, are presented. The recurrent neuro-fuzzy network, forms a "global" nonlinear long-range prediction model through the fuzzy conjunction of a number of "local" linear dynamic models. Network output is fed back to network input through one or more time delay units, which ensure that predictions from the recurrent neuro-fuzzy network are long-range. In building a recurrent neural network model, process knowledge is used initially to partition the processes non-linear characteristics into several local operating regions, and to aid in the initialisation of corresponding network weights. Process operational data is then used to train the network. Membership functions of the local regimes are identified, and local models are discovered via network training. Based on a recurrent neuro-fuzzy network model, a multi-objective optimal control policy can be obtained. The proposed technique is applied to a fed-batch reactor.  相似文献   
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The minimum flow requirements in the Svartå River in Sweden are directed at maintaining fishlife and providing suitable dilution for waste flows. The implications of varying the minimum flow requirements in the river are examined using a mixed integer optimisation model. The model is formulated as a modified method-of-weights technique with the economic issues of hydro-electricity generation, irrigation and urban water supply placed in the objective function and the minimum flows specified within the constraint set. The integer component of the model is required to model the operating policy at the major flow regulation facility in the system and the restricted validity of the irrigation permits. Application of the model shows that in dry years where competition between minimum flow levels and the other economic uses, is most intense, the levels achieved by the various economic objectives are only slightly reduced even with significant increases in the minimum flow requirements. Variations in minimum flow requirements of up to 45% only produce changes of 10% or less in the economic objectives. The lack of sensitivity of the objective levels is due primarily to the level of control exerted indirectly on the whole system in dry years by the release regulation policy and the restricted validity of the irrigation permits. In normal to wet years these policies are not as restrictive and more choice is available. In such years, however, there is generally sufficient water to satisfy all requirements and allocation is not a critical issue. The model itself is formulated generally so that a range of scenarios beyond those examined specifically in the paper can be considered.  相似文献   
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An assembly supply chain (SC) is composed of stages that provide the components, assemble both sub-assemblies and final products, and deliver products to the customer. The activities carried out in each stage could be performed by one or more options, thus the decision-maker must select the set of options that minimises the cost of goods sold (CoGS) and the lead time (LT), simultaneously. In this paper, an ant colony-based algorithm is proposed to generate a set of SC configurations using the concept of Pareto optimality. The pheromones are updated using an equation that is a function of the CoGS and LT. The algorithm is tested using a notebook SC problem, widely used in literature. The results show that the ratio between the size of the Pareto Front computed by the proposed algorithm and the size of the one computed by exhaustive enumeration is 90%. Other metrics regarding error ratio and generational distance are provided as well as the CPU time to measure the performance of the proposed algorithm.  相似文献   
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Based on the current model of a phosphoric acid fuel cell (PAFC) system, the electrolyte concentration is optimised. New analytical expressions for the power output and efficiency of the PAFC system are derived by considering the effects of multi-irreversibilities resulting from the activation overpotential, concentration overpotential, ohmic overpotential, and leakage resistance on the performance of the PAFC system. These parameters are used to evaluate the general performance characteristics of the PAFC system. Accordingly, the upper and lower limits of the optimised values for some main parameters, such as the current density, power output, and efficiency, are determined. Moreover, a multi-objective function, including both the power output and efficiency, is introduced and used to further subdivide the parametric optimum regions according to different requirements. In addition, a general formula for the load of the system is derived. The relations between the power output and efficiency of the system and the load are discussed in detail, and the optimum matching conditions of the load are obtained.  相似文献   
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航班着陆调度问题是多目标优化问题,难以使用最优化方法求解。为了解决这一难题,以减少航班延迟时间和降低飞行延误成本为目标,提出一种整合的启发式方法。该方法使用吱呀轮算法SWO(Squeaky-Wheel Optimization)进行导向式搜索,并利用改进的GA充分扩展SWO的搜索空间,最后通过合理整合GA和SWO,取得求解效率和求解质量的提高。通过实验仿真对比表明该算法能高效求解该问题,满足了实时调度的需求,同时求解质量也优于其他启发式算法,节省了更多降落时间和成本。  相似文献   
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针对多目标优化问题提出一种自适应混沌混合蛙跳算法 MACSFLA(Adaptive chaos shuffled frog leaping algorithm for mul-tiobjective optimization)。使用动态权重因子策略以提高混合蛙跳算法 SFLA(Shuffled Frog Leaping Algorithm)收敛效率,引入基于 Pa-reto 支配能力的 SFLA 子族群划分策略,使得 SFLA 能够应用于多目标优化问题。在此基础上,MACSFLA 首先利用 SFLA 快速寻优能力接近理论 Pareto 最优解,然后采用自适应网格密度机制动态维护外部存储器 Pareto 最优解规模,并使用自适应混沌优化技术改善 Pareto 最优解集样本多样性,最后利用 Pareto 最优解选择策略为青蛙种群选择最优更新粒子。多目标函数测试实验结果表明,与MOPSO 和 NSGA-Ⅱ相比,MACSFLA 在 Pareto 最优解集均匀性和多样性上有明显优势。  相似文献   
9.
为了搜索空域扇区优化中的满意解,结合计算几何和模拟退火算法对空域扇区优化问题进行了研究。根据管制空域结构和交通流量空间分布,建立空域扇区分割的模糊多目标函数和约束条件函数,提出划设空域的二分策略,并结合模拟退火算法对扇区优化划设问题进行求解。实例分析表明,结合二分策略的模拟退火方法可获得满意解,扇区划设多目标优化的总体满意度比仅考虑均衡扇区平均流量时提高了2.1%。  相似文献   
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多目标演化算法的研究目标是使算法种群快速收敛并均匀分布于问题的非劣最优域。根据个体的非支配排序级数设计了一种自适应变异步长的柯西变异算子,对变异越界处理进行了改进;并定义和使用动态拥挤距离来保持群体中个体的均匀分布。最后通过对测试函数的实验,验证了算法的可行性和有效性。  相似文献   
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