基于神经网络和遗传算法的锭子弹性管性能优化

为得到减振弹性管对下锭胆的支承弹性和锭子高速运动下的稳定性等性能的最优匹配效率,依据减振弹性管的等效抗弯刚度及底部等效刚度系数公式,利用MatLab数值分析软件构建弹性管抗弯刚度和底部挠度数学模型。首先,结合Isight优化软件基于径向基神经网络构建其近似模型,且使精度达到可接受水平,并以模型的关键结构参数弹性模量、螺距、槽宽、壁厚为设计变量,结合遗传算法对弹性管抗弯刚度和底部挠度进行多目标优化设计,得到Pareto最优解集和Pareto前沿图,确定出减振弹性管结构工艺参数的优化方案。通过对优化数据进行分析发现,该方案在保证减振弹性管弹性的同时,其底部振幅明显减弱。     

A knowledge-based archive multi-objective simulated annealing algorithm to optimize series–parallel system with choice of redundancy strategies

Redundancy allocation problem (RAP) is one of the best-developed problems in reliability engineering studies. This problem follows to optimize the reliability of a system containing s sub-systems under different constraints, including cost, weight, and volume restrictions using redundant components for each sub-system. Various solving methodologies have been used to optimize this problem, including exact, heuristic, and meta-heuristic algorithms. In this paper, an efficient multi-objective meta-heuristic algorithm based on simulated annealing (SA) is developed to solve multi-objective RAP (MORAP). This algorithm is knowledge-based archive multi-objective simulated annealing (KBAMOSA). KBAMOSA applies a memory matrix to reinforce the neighborhood structure to achieve better quality solutions. The results analysis and comparisons demonstrate the performance of the proposed algorithm for solving MORAP.     

SDN中基于KMOBPSO的高可靠性控制器部署算法

针对SDN中控制器系统的单节点故障问题,兼顾系统成本和系统时延,应用N+1冗余备份模型来提高SDN控制器部署的可靠性,并将其抽象为多目标优化问题.同时,提出了一种融合K-means聚类算法和遗传算子的多目标二进制粒子群算法——KMOBPSO算法,以求解SDN控制器高可靠性部署问题的解.仿真结果表明,所提算法具有求解精度高、分布均匀、沿Pareto前沿面覆盖广的特点,能够显著提高SDN中控制器部署的可靠性.     

Multi-objective particle swarm optimisation based integrated production inventory routing planning for efficient perishable food logistics operations

Sustainable and efficient food supply chain has become an essential component of one’s life. The model proposed in this paper is deeply linked to people's quality of life as a result of which there is a large incentive to fulfil customer demands through it. This proposed model can enhance food quality by making the best possible food quality accessible to customers, construct a sustainable logistics system considering its environmental impact and ensure the customer demand to be fulfilled as fast as possible. In this paper, an extended model is examined that builds a unified planning problem for efficient food logistics operations where four important objectives are viewed: minimising the total expense of the system, maximising the average food quality along with the minimisation of the amount of CO₂ emissions in transportation along with production and total weighted delivery lead time minimisation. A four objective mixed integer linear programming model for intelligent food logistics system is developed in the paper. The optimisation of the formulated mathematical model is proposed using a modified multi-objective particle swarm optimisation algorithm with multiple social structures: MO-GLNPSO (Multi-Objective Global Local Near-Neighbour Particle Swarm Optimisation). Computational results of a case study on a given dataset as well as on multiple small, medium and large-scale datasets followed by sensitivity analysis show the potency and effectiveness of the introduced method. Lastly, there has been a scope for future study displayed which would lead to the further progress of these types of models.     

城市规划中的水资源承载力计算——以惠州市为例

探讨水资源承载力综合评估和多目标计算理论在城市规划中的应用。运用灰色多层次综合评估模型对惠州市水资源承载力进行定性的综合评价,应用多目标规划理论对城市经济发展规划不同方案下的惠州市水资源承载力作了定量计算。     

安徽省水资源宏观经济优化模型研究

以水资源可持续发展为出发点,探讨了一种可以同时兼顾经济发展、水资源利用和社会效益的多目标水资源宏观经济模型的建立方法,通过引入带权2-模评价函数,将多目标优化问题转变成单目标优化问题。以2002年为现状水平年,对安徽省2010年的产业结构及产业规模进行优化分析。结果表明,多目标模型求得的总用水量较单目标模型明显降低,而两模型优化的产业规模与结构差别不大,体现了高用水效率行业优先发展的要求。     

动态多目标决策问题的灰色分析方法

将用于固定时间截面下静态多目标决策的灰色关联理论推广到动态情形，引入局部理想最优效果和整体理想最优效果的概念，提出一种新型的动态多目标决策问题的灰色关联模型，并通过算例说明该方法的合理可行性。     

多目标模糊全局优化设计方法和软件

分析了优化设计在土木工程中应用不够广泛的原因，由此提出了一个能用于工程设计的多目标模糊全局优化设计方法。介绍了按该方法研制的软件ＭＦＤ的功能和性能，以及一个用该软件分析的拱坝体型设计实例。     

Minimum spanning trees made easier via multi-objective optimization

Many real-world problems are multi-objective optimization problems and evolutionary algorithms are quite successful on such problems. Since the task is to compute or approximate the Pareto front, multi-objective optimization problems are considered as more difficult than single-objective problems. One should not forget that the fitness vector with respect to more than one objective contains more information that in principle can direct the search of evolutionary algorithms. Therefore, it is possible that a single-objective problem can be solved more efficiently via a generalized multi-objective model of the problem. That this is indeed the case is proved by investigating the computation of minimum spanning trees.This work was supported by the Deutsche Forschungsgemeinschaft (DFG) as part of the Collaborative Research Center Computational Intelligence (SFB 531) and by the German-Israeli Foundation (GIF) in the project Robustness Aspects of Algorithms.     

基于模糊控制原理的多目标决策方法

在模糊控制原理的基础上,对于含有定性变量的多目标决策问题提出了一种新的求解方法:先把目标值与权重进行模糊化,再通过模糊推理和反模糊化过程直接得到各方案的评价值,进而进行多目标决策.具体实例的计算结果表明,该方法具有简单、实用、直观的优点.