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1.
一种多目标优化的多概率模型分布估计算法   总被引:1,自引:1,他引:0  
钟润添  龚海峰  李斌  庄镇泉 《计算机仿真》2007,24(4):180-182,234
提出了一种用于多目标优化的多概率模型分布估计算法,该算法在进化的每一代中使用多个概率模型来引导多目标优化问题柏拉图(Pareto)最优域的搜索.分布估计算法使用概率模型引导算法最优解的搜索,而使用多个概率模型可以保持所得多目标优化问题最优解集的多样性.该算法具有很强的寻优能力,所得结果可以很好地覆盖Pareto前沿.实验通过优化一组测试函数来评价该算法的性能,并与其它多目标优化算法进行了比较,结果表明该算法相比于其它同类算法可以更好地解决多目标优化问题.  相似文献   
2.
The multiobjective differential evolution (MODE), which is an extension of the Differential Evolution (DE), is applied to solve the multiobjective optimization problem (MOOP) of wet film Poly-Ethylene Terephthalate (PET) reactor considering minimization of acid end group and vinyl end group as the main objectives. The objective function is modified to solve five different possible cases. The results show that a Pareto set (a set of equally good solutions) is obtained for the cases when two of the parameters (residence time of the polymeric reaction mass, θ, and the speed of the wiped-film agitator, N) are considered as decision variables, unlike a unique solution obtained using Nondominated Sorting Genetic Algorithm (NSGA). The Pareto optimal front provides wide-ranging optimal sets of operating conditions. And an appropriate set of operating conditions can be selected based on the requirements of the user.  相似文献   
3.
尚荣华  焦李成  公茂果  马文萍 《软件学报》2007,18(11):2700-2711
求解动态多目标优化(dynamic multi-objective optimization,简称DMO)问题的主要困难在于目标函数、约束条件或者相关的问题参数是随时间不断变化的.基于免疫克隆选择学说,提出一种用于解决DMO问题的新算法--动态多目标免疫克隆优化(immune clonal algorithm for DMO,简称ICADMO).该算法改进了现有的克隆策略,采用整体克隆的方式;在选择策略上,根据Pareto-占优的概念,将抗体群中的个体分为支配个体和非支配个体,对非支配个体进行选择.采用3个特色算子,使其很好地保持了所得解的多样性、均匀性和收敛性.通过数值实验,与DBM(direction-based method)算法进行比较,结果表明,新算法在收敛性、多样性以及解分布的广度方面都体现了很好的性能.  相似文献   
4.
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   
5.
6.
吴亚丽  徐丽青 《控制与决策》2012,27(8):1127-1132
提出一种基于粒子群算法的改进多目标文化算法并用于求解多目标优化问题.算法中群体空间采用多目标粒子群优化算法进行演化;信念空间通过对形势知识、规范化知识和历史知识的重新定义使之符合多目标优化问题;信念空间和群体空间的交互通过自适应的接受操作和影响操作来实现.若干多目标标准测试函数的仿真结果表明,改进多目标文化算法能够在保持Pareto解集多样性的同时具有较好的均匀性和收敛性.  相似文献   
7.
采用改进型多目标粒子群算法的电力系统环境经济调度   总被引:3,自引:1,他引:2  
电力系统多目标环境经济调度要求在满足发电成本最小的同时发电厂的污染气体排放也最小,为此提出了基于Pareto占优策略和拥挤距离排序方法的改进型粒子群算法求解该多目标问题。采用容量可动态调整的外部存档集合存储当前Pareto最优解,利用Pareto占优策略确定个体最优位置,进而根据粒子拥挤距离确定全局最优位置,并设置了动态惯性权重,引入了小概率变异机制,提高了算法搜索能力。算例结果验证了该算法的有效性。  相似文献   
8.
The work presents two approaches for the construction of empirical class-models for a given category C. The attention is centred on the information provided by the sensitivity and specificity, the two usual parameters employed to qualify a class-model. In fact, not only a class-model is built for C but a set of class-models which differ in their sensitivity and specificity. Therefore the range of possible jointly available values is described, allowing the user to select the model that best adapt to specific situations or particular needs.One of the approaches, PLS-CM (Partial Least Squares Class-Modelling), is based on the modelling of the distribution of the values obtained by a PLS model fitted with binary response (belong/do not belong to C). In that way, the corresponding hypothesis test permits the computation of the probabilities α and β of type I and type II errors when deciding whether a sample belongs to C. These probabilities, expressed as percentages, are 100 minus sensitivity and 100 minus specificity, respectively. The representation of β versus α is the risk curve that describes the PLS-CM capability of modelling category C.The other approach comes from setting the problem as a multi-objective optimization problem, the one that corresponds to simultaneously maximize sensitivity and specificity, which usually behave oppositely. The trading-off solutions (again, different class-models) are computed to be Pareto-optimal solutions, that is, the set of the optimal solutions in at least one of the conflicting objectives, what is known as the Pareto-optimal front, POF.Additionally, a procedure to cross-validate the risk curve and the Pareto-optimal front is proposed for the first time in order to evaluate the prediction ability of both methods.Two case-studies are used to drive the discussion: 1) the characterization of wines that official wine-tasters regarded as compliant ones according to the quality characteristics stated by a Denomination of Origin and 2) The characterization of breast tumours defined as benign (compliant class) from 9 cytological variables.Finally, the performance of the methods is tested using several data sets from the literature.  相似文献   
9.
A new multiobjective particle swarm optimization (MOPSO) technique for environmental/economic dispatch (EED) problem is proposed in this paper. The proposed MOPSO technique evolves a multiobjective version of PSO by proposing redefinition of global best and local best individuals in multiobjective optimization domain. The proposed MOPSO technique has been implemented to solve the EED problem with competing and non-commensurable cost and emission objectives. Several optimization runs of the proposed approach have been carried out on a standard test system. The results demonstrate the capabilities of the proposed MOPSO technique to generate a set of well-distributed Pareto-optimal solutions in one single run. The comparison with the different reported techniques demonstrates the superiority of the proposed MOPSO in terms of the diversity of the Pareto-optimal solutions obtained. In addition, a quality measure to Pareto-optimal solutions has been implemented where the results confirm the potential of the proposed MOPSO technique to solve the multiobjective EED problem and produce high quality nondominated solutions.  相似文献   
10.
A mixed (electroosmotic and pressure-driven) flow microchannel heat sink has been studied and optimized with the help of three-dimensional numerical analysis, surrogate methods, and the multi-objective evolutionary algorithm. Two design variables; the ratio of the microchannel width-to-depth and the ratio of fin width-to-depth of the microchannel are selected as the design variables while design points are selected through a four-level full factorial design. The single-objective optimization is performed taking overall thermal resistance as the objective function and Radial Basis Neural Network as the surrogate model while for multi-objective optimization pumping power is considered as the objective function along with the thermal resistance. It is observed that the optimum design shifted towards the lower values of the ratio of the channel width-to-depth and the higher values of the ratio of fin width-to-depth of channel with increase of the driving source. The trade-off between the two conflicting objectives has been found and discussed in detail in light of the distribution of Pareto-optimal solutions in the design space. The ratio of channel width-to-depth is found to be higher Pareto-sensitive (sensitivity along the Pareto-optimal front) than the ratio of fin width-to-depth of the channel.  相似文献   
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