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具有稳定系数的多目标多维模糊决策算法 总被引:1,自引:0,他引:1
对多目标多维模糊决策模型的模糊交叉算法做了进一步研究,分析了多目标多维模糊决策模型中主观监督因子在三维以上模糊决策时对目标权重调节不灵敏的原因,提出一种新的模糊环境下带有目标权重主观监督因子和稳定系数的目标函数,给出了具有稳定系数和主观监督因子的目标权重计算公式,并给出了多目标多维模糊决策的算法,实例计算说明了算法的有效性。 相似文献
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监督模糊模式识别交叉迭代模型 总被引:2,自引:0,他引:2
从模糊模式识别概念出发,建立一种以决策者经验、偏好为监督,在方案优属等级识别过程中确定最佳目标权重和方案优属度的监督模糊模式识别交叉迭代算法,该算法集成了决策偏好信息完全未知、部分未知、完全已知的主客观权重识别方法。并严格证明了该算法的局部收敛性。 相似文献
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多目标多维模糊决策模型的模糊交叉算法 总被引:3,自引:1,他引:3
对多目标多维模糊决策模型做了进一步研究,提出一种模糊环境下带有目标权重主观监督因子的目标函数,提出了计算模糊决策识别矩阵与目标权重的模糊交叉计算公式。该算法既充分利用了模糊决策中的客观信息,又充分利用了决策专家的知识与经验,将客观定权与主观定权有机结合起来,为求解最优模糊决策识别矩阵和确定目标最优权重提供了一种有效途径,进一步丰富了多目标多维模糊决策理论模型。将本文提出的模糊决策方法应用于16家电炉炼钢企业的模糊综合评价决策,取得了较为满意的效果。 相似文献
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针对目标函数估值昂贵的多目标优化问题,提出了基于聚类的代理辅助进化算法。在MOEA/D算法的框架下,对种群进行聚类,并通过权重向量的邻域选出种群子集,在子集上使用径向基插值函数辅助的差分进化算法得到新解,对种群进行更新。在7个DTLZ标准测试问题上进行了数值实验,计算结果表明本文提出的算法比新近提出的多目标邻域回归优化(MONRO)算法具有优势。 相似文献
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针对重大突发事件的应急物资救援,研究了应急物流中心的选址及应急物资的调运问题。利用离散的情景集合描述受灾点应急物资需求的不确定性以及应急物资运输成本和运输时间的不确定性,同时考虑应急救援成本和应急救援时间两个目标,建立了多目标应急物流中心选址的确定型模型和鲁棒优化模型。为将多目标问题转化为单目标问题,利用成本单目标和时间单目标的最优结果将多目标转化为相对值再加权处理,该方法既可消除多个目标之间的单位及数量级差异,还可以根据问题的数据变化进行动态调整。以提供应急物资救援服务的设施作为编码,设计了一种通用的混合蛙跳算法。为检验模型和算法的有效性,设计了一个多情景的算例,结果表明两个模型和算法具备良好的可行性和有效性,且鲁棒优化模型能较好地保持对各种不确定性的抗干扰能力;最后,讨论分析了成本偏好权重和鲁棒约束系数的影响,结果表明可根据成本偏好权重的取值范围来区分各种应急救援阶段,体现不同救援阶段的救援要求及特征,并给出了成本偏好权重和鲁棒约束系数的取值建议。 相似文献
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关于求解DEA原始CCR模型中最优输入输出权重的方法 总被引:7,自引:0,他引:7
本文给出了求解DEA原始CCR模型中最优输入输出权重的简便方法:首先将原始CCR模型化为线性规划模型,然后从该线性规划模型的对偶模型入手,运用单纯形法,在得到决策单元最优效率评价指数时,根据线性规划的对偶理论,得到决策单元最优输入输出权重。该权重可用在逆DEA新算法中。 相似文献
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本文在更为广泛的权函数和边界函数的范围内,使用不高于独立列场合下的矩条件,讨论了NA列的一类小参数级数的极限状态及收敛速度.因此,本文的结果即使对独立列也是有意义的. 相似文献
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A rank-one algorithm is presented for unconstrained function minimization. The algorithm is a modified version of Davidon's variance algorithm and incorporates a limited line search. It is shown that the algorithm is a descent algorithm; for quadratic forms, it exhibits finite convergence, in certain cases. Numerical studies indicate that it is considerably superior to both the Davidon-Fletcher-Powell algorithm and the conjugate-gradient algorithm. 相似文献
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针对恒模算法(CMA)收敛速度较慢、收敛后均方误差较大的缺点,提出一种新的双模式盲均衡算法.在算法初期,利用能快速收敛的归一化恒模算法(NCMA)进行冷启动,在算法收敛后切换到判决引导(DD-LMS)算法,减少误码率.计算机仿真表明,提出的新算法有较快的收敛速度和较低的误码率. 相似文献
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We describe a fraction free version of the Matrix Berlekamp/Massey algorithm. The algorithm computes a minimal matrix generator of linearly generated square matrix sequences in an integral domain. The algorithm performs all operations in the integral domain, so all divisions performed are exact. For scalar sequences, the matrix algorithm specializes to a different algorithm than the algorithm currently in the literature. This new scalar algorithm has smaller intermediate values than the known fraction free Berlekamp/Massey algorithm. 相似文献
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Yaguang Yang 《Numerical Algorithms》2017,74(4):967-996
Mehrotra’s algorithm has been the most successful infeasible interior-point algorithm for linear programming since 1990. Most popular interior-point software packages for linear programming are based on Mehrotra’s algorithm. This paper describes a proposal and implementation of an alternative algorithm, an arc-search infeasible interior-point algorithm. We will demonstrate, by testing Netlib problems and comparing the test results obtained by the arc-search infeasible interior-point algorithm and Mehrotra’s algorithm, that the proposed arc-search infeasible interior-point algorithm is a more reliable and efficient algorithm than Mehrotra’s algorithm. 相似文献
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In this paper, we present a long-step primal path-following algorithm and prove its global convergence under usual assumptions. It is seen that the short-step algorithm is a special case of the long-step algorithm for a specific selection of the parameters and the initial solution. Our theoretical result indicates that the long-step algorithm is more flexible. Numerical results indicate that the long-step algorithm converges faster than the short-step algorithm. 相似文献
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Antoine Jouglet Ceyda Oğuz Marc Sevaux 《Journal of Mathematical Modelling and Algorithms》2009,8(3):271-292
The paper considers the hybrid flow-shop scheduling problem with multiprocessor tasks. Motivated by the computational complexity
of the problem, we propose a memetic algorithm for this problem in the paper. We first describe the implementation details
of a genetic algorithm, which is used in the memetic algorithm. We then propose a constraint programming based branch-and-bound
algorithm to be employed as the local search engine of the memetic algorithm. Next, we present the new memetic algorithm.
We lastly explain the computational experiments carried out to evaluate the performance of three algorithms (genetic algorithm,
constraint programming based branch-and-bound algorithm, and memetic algorithm) in terms of both the quality of the solutions
produced and the efficiency. These results demonstrate that the memetic algorithm produces better quality solutions and that
it is very efficient. 相似文献
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This paper presents a new composite sub-steps algorithm for solving reliable numerical responses in structural dynamics. The newly developed algorithm is a two sub-steps, second-order accurate and unconditionally stable implicit algorithm with the same numerical properties as the Bathe algorithm. The detailed analysis of the stability and numerical accuracy is presented for the new algorithm, which shows that its numerical characteristics are identical to those of the Bathe algorithm. Hence, the new sub-steps scheme could be considered as an alternative to the Bathe algorithm. Meanwhile, the new algorithm possesses the following properties: (a) it produces the same accurate solutions as the Bathe algorithm for solving linear and nonlinear problems; (b) it does not involve any artificial parameters and additional variables, such as the Lagrange multipliers; (c) The identical effective stiffness matrices can be obtained inside two sub-steps; (d) it is a self-starting algorithm. Some numerical experiments are given to show the superiority of the new algorithm and the Bathe algorithm over the dissipative CH-α algorithm and the non-dissipative trapezoidal rule. 相似文献
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Stochastic global search algorithms such as genetic algorithms are used to attack difficult combinatorial optimization problems.
However, genetic algorithms suffer from the lack of a convergence proof. This means that it is difficult to establish reliable
algorithm braking criteria without extensive a priori knowledge of the solution space. The hybrid genetic algorithm presented here combines a genetic algorithm with simulated
annealing in order to overcome the algorithm convergence problem. The genetic algorithm runs inside the simulated annealing
algorithm and provides convergence via a Boltzmann cooling process. The hybrid algorithm was used successfully to solve a
classical 30-city traveling salesman problem; it consistently outperformed both a conventional genetic algorithm and a conventional
simulated annealing algorithm.
This work was supported by the University of Colorado at Colorado Springs. 相似文献