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1.
王金华  尹泽勇 《计算机应用》2007,27(11):2817-2830
用多目标粒子群优化(MOPSO)算法的粒子位置更新模式替代NSGA Ⅱ的交叉操作,获得一个新的算法(NSGA Ⅱ MOPSO)。为使这两种差异较大的算法实现无缝融合,在NSGA Ⅱ算法范围内对MOPSO中特有的概念粒子及其速度、Pbest、引导者进行处理: 1)粒子对应于NSGA Ⅱ中子代群体的个体; 2)不再使用粒子速度概念; 3)不再使用粒子Pbest概念,代之以从父代群体中为每个粒子的每一维寻找一个最近的该粒子非支配个体; 4)每一个粒子的引导者可以是父代群体中稀疏程度最大的个体或者是按照二进制随机竞赛选择方法从父代群体中选择的一个个体,具体哪一种方式发挥作用依赖于预先设定的概率。另外,引入稀疏程度概念来评价粒子在目标函数空间的分布。6个算例的结果表明,与NSGA Ⅱ及最新的两种MOPSO算法(CLMOPSO 和 EM MOPSO)相比,新算法是一个有效、稳定的算法。  相似文献   
2.
提出了一种基于适应度空间距离评估选取最优解的多目标粒子群算法。该方法避免了目前多目标优化求解方法中权重选择的难题,保证了寻优方向的多向性,可以获得多目标优化问题的Pareto解集。将该算法应用于网损最小、静态电压稳定裕度最大为目标的多目标无功优化问题,算例表明在有效性和最优性等方面均有良好表现。  相似文献   
3.
In this paper, a bi-objective multi-products economic production quantity (EPQ) model is developed, in which the number of orders is limited and imperfect items that are re-workable are produced. The objectives of the problem are minimization of the total inventory costs as well as minimizing the required warehouse space. The model is shown to be of a bi-objective nonlinear programming type, and in order to solve it two meta-heuristic algorithms namely, the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) algorithm, are proposed. To verify the solution obtained and to evaluate the performance of proposed algorithms, two-sample t-tests are employed to compare the means of the first objective value, the means of the second objective values, and the mean required CPU time of solving the problem using two algorithms. The results show while both algorithms are efficient to solve the model and the solution qualities of the two algorithms do not differ significantly, the computational CPU time of MOPSO is considerably lower than that of NSGA-II.  相似文献   
4.
Reliability problems are an important type of optimization problems that are motivated by different needs of real-world applications such as telecommunication systems, transformation systems, and electrical systems, so on. This paper studies a special type of these problems which is called redundancy allocation problem (RAP) and develops a bi-objective RAP (BORAP). The model includes non-repairable series–parallel systems in which the redundancy strategy is considered as a decision variable for individual subsystems. The objective functions of the model are (1) maximizing system reliability and (2) minimizing the system cost. Meanwhile, subject to system-level constraint, the best redundancy strategy among active or cold-standby, component type, and the redundancy level for each subsystem should be determined. To have a more practical model, we have also considered non-constant component hazard functions and imperfect switching of cold-standby redundant component. To solve the model, since RAP belong to the NP-hard class of the optimization problems, two effective multi-objective metaheuristic algorithms named non-dominated sorting genetic algorithms (NSGA-II) and multi-objective particle swarm optimization (MOPSO) are proposed. Finally, the performance of the algorithms is analyzed on a typical case and conclusions are demonstrated.  相似文献   
5.
One of the most fascinating aerospace problems today is the optimisation of interplanetary trajectories in the Solar System, using gravitational fly-bys in order to reduce fuel consumption and/or mission duration. This is a complex optimisation problem due to the complexity of the models and the lack of analytical solutions, as well as to the presence of strong discontinuities. An exhaustive search of the space of input variables is unaffordable even with modern, state-of-the-art computing technology. Thus, a feasible approach requires artificial intelligence and modern optimisation techniques based on the intelligent selection of some potential solutions. These individuals evolve in order to generate better solutions until some optimisation criteria are met. In this work, two evolutive algorithms are considered, both based on particle swarm optimisation. In both cases, the trajectory to be optimised departs from Earth and, after a fly-by in Mars, arrives to Jupiter. In the single-criteria case, only the fuel consumption is considered as a variable to be minimised, while in the multi-criteria case the fuel consumption and the total time of the mission are simultaneously taken into account, building a Pareto-front of non-dominated solutions that provides an interesting view of the possible options for the space mission. In both, the single- and multi-criteria cases, the swarm algorithms optimise some tuning parameters of the trajectory: launch epoch, and times of flight between planets. Results are compared to other works on the same problem. They demonstrate the benefit of applying these evolutive techniques to decrease both mission duration and propellant cost when using intermediate gravity assist bodies.  相似文献   
6.
区域新能源大规模的发展,其中主要目标之一是为了提高系统新能源接入规模。为此文中构建了风-光-水互补系统优化运行的数学模型,考虑为一个送端区域系统,构建了相对标准偏差作为衡量波动性指标。将风机和光伏电池的个数作为变量,以新能源接入规模最大和输入主网波动性指标最小作为目标函数,综合考虑各类约束条件,并且采用多目标粒子群优化算法进行求解。最后,在IEEE-14节点系统中,以青海某地区资源为实例进行建模、求解和分析,结果验证了所提出优化配置方法的可行性。  相似文献   
7.
随着负荷峰谷差增大,电力系统调峰困难问题日益突出.抽水蓄能电站具有启停迅速、削峰填谷等良好的运行特性,可以作为调峰电源优化负荷曲线,减小负荷峰谷差和波动性.建立了以抽水蓄能电站运行后的等效负荷曲线峰谷差减小、曲线更为平滑为目标的多目标抽水蓄能电站优化调度模型.利用引入Maxmin函数和ε支配的改进多目标粒子群算法对模型进行求解.通过算例验证了本文所提模型和算法的有效性和合理性.  相似文献   
8.
孙欣  那彦  王赟鹏 《电子科技》2014,27(10):40-42
在多传感器图像融合研究领域,基于频域的多尺度变换融合算法是研究的热点,文中提出一种基于多目标粒子群优化算法,对已有的基于Contourlet变换融合方法得到的融合结果图像集,在空域进行进一步处理的图像融合算法。实验结果表明,该算法具有较好的融合效果。  相似文献   
9.
为了改善汽油机燃用丁醇-汽油混合燃料后带来的发动机性能下降的问题,采用改进的多目标粒子群算法对燃烧影响因素进行了优化.首先建立了丁醇汽油发动机整机模型,并对比实验数据验证了模型的可靠性;然后提出了一种适应丁醇汽油混合燃料的改进多目标粒子群算法;随后以发动机的配气相位、空燃比、点火提前角等参数为设计变量,综合发动机燃油消耗率和扭矩作为目标函数,应用改进的多目标粒子群算法进行了优化.结果表明改进的多目标粒子群算法能快速找到优化结果,优化后的发动机在燃用丁醇汽油时性能得到较大的提高.  相似文献   
10.
在深入研究图像融合算法的基础上,受多目标粒子群优化算法(MOPSO)的启发,提出了一种改进的MOPSO算法,并将该改进算法用于图像融合方面。这种算法提出了两次调节指数收敛函数,使得寻优速率得到更为平滑地过渡,从而让搜索结果更好的接近Pareto最优解集。实验结果表明,与传统的融合算法比较在客观性能指标上得到提高。  相似文献   
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