共查询到19条相似文献,搜索用时 93 毫秒
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本文针对一类连续非线性Max-Min优化所对应的鞍点问题,提出了一种交替投影算法,证明了算法的收敛性.初步的数值实验表明本文所提出的算法比已有的同类算法具有更高的计算效率. 相似文献
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填充函数法是求解多变量、多极值函数全局优化问题的有效方法.这种方法的关键是构造填充函数.本文在无Lipschitz连续条件下,对一般无约束最优化问题提出了一类单参数填充函数.讨论了其填充性质,并设计了一个求解约束全局优化问题的填充函数算法,数值实验表明,算法是有效的. 相似文献
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0 引言现实世界的许多优化问题属于动态优化问题一类.我们求解此类问题的目标,是设计一种自适应算法,能够在变化的问题环境中连续追踪最优解.演化算法是基于自然界 相似文献
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针对一类可分离凸优化问题提出了一种非精确平行分裂算法.该算法充分利用了所求解问题的可分离结构,并对子问题进行非精确求解.在适当的条件下,证明了所提出的非精确平行分裂算法的全局收敛性,初步的数值实验说明了算法有效性. 相似文献
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针对一类特殊的多目标优化问题,其每个目标函数为一个二阶连续可微凸函数与一个真凸但不必可微函数之和,提出了邻近牛顿法.我们引入了带线搜索的邻近牛顿法和不带线搜索的邻近牛顿法.在适当的条件下,我们证明了由这两类算法产生的序列的每个聚点是多目标优化问题的Pareto平稳点.此外,我们给出了它们在约束多目标优化和鲁棒多目标优化中的应用.特别地,对于鲁棒多目标优化,我们证明了邻近牛顿法的子问题可以看作二次规划问题.对此,我们还进行了数值实验,验证了该方法的有效性. 相似文献
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针对不连续无约束全局优化问题,构造且运用对数变差积分来进行研究和求解.具体给出了对数变差积分函数的分析性质及其全局优化问题的最优性条件和概念性算法.结合Monte-Carlo技术,特别针对n=100个变量、具有不连续目标函数的三个具体实例进行了数值试验,计算结果也表明所给方法的可行性和有效性. 相似文献
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Shangyao Yan Der-shin Juang Chien-rong Chen Wei-shen Lai 《Journal of Global Optimization》2005,33(1):123-156
Traditionally, the minimum cost transshipment problems have been simplified as
linear cost problems, which are not practical in real applications. Recently, some advanced
local search algorithms have been developed that can directly solve concave cost bipartite
network problems. However, they are not applicable to general transshipment problems.
Moreover, the effectiveness of these modified local search algorithms for solving general
concave cost transshipment problems is doubtful. In this research, we propose a global search algorithm for solving concave
cost transshipment problems. Effecient methods for encoding, generating initial populations, selection, crossover and mutation
are proposed, according to the problem characteristics. To evaluate the effectiveness of the proposed global search algorithm,
four advanced local search algorithms based on the threshold accepting algorithm, the great deluge algorithm, and the tabu
search algorithm, are also developed and are used for comparison purpose. To assist with the comparison of the proposed algorithms,
a randomized network generator is designed to produce test problems. All the tests are performed on a personal computer. The
results indicate that the proposed global search algorithm is more effective than the four advanced local algorithms, for
solving concave cost transshipment problems. 相似文献
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This paper describes the development of a novel metaheuristic that combines an electromagnetic-like mechanism (EM) and the
great deluge algorithm (GD) for the University course timetabling problem. This well-known timetabling problem assigns lectures
to specific numbers of timeslots and rooms maximizing the overall quality of the timetable while taking various constraints
into account. EM is a population-based stochastic global optimization algorithm that is based on the theory of physics, simulating
attraction and repulsion of sample points in moving toward optimality. GD is a local search procedure that allows worse solutions
to be accepted based on some given upper boundary or ‘level’. In this paper, the dynamic force calculated from the attraction-repulsion
mechanism is used as a decreasing rate to update the ‘level’ within the search process. The proposed method has been applied
to a range of benchmark university course timetabling test problems from the literature. Moreover, the viability of the method
has been tested by comparing its results with other reported results from the literature, demonstrating that the method is
able to produce improved solutions to those currently published. We believe this is due to the combination of both approaches
and the ability of the resultant algorithm to converge all solutions at every search process. 相似文献
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When solving multi-objective optimization problems (MOPs) with big data, traditional multi-objective evolutionary algorithms (MOEAs) meet challenges because they demand high computational costs that cannot satisfy the demands of online data processing involving optimization. The gradient heuristic optimization methods show great potential in solving large scale numerical optimization problems with acceptable computational costs. However, some intrinsic limitations make them unsuitable for searching for the Pareto fronts. It is believed that the combination of these two types of methods can deal with big MOPs with less computational cost. The main contribution of this paper is that a multi-objective memetic algorithm based on decomposition for big optimization problems (MOMA/D-BigOpt) is proposed and a gradient-based local search operator is embedded in MOMA/D-BigOpt. In the experiments, MOMA/D-BigOpt is tested on the multi-objective big optimization problems with thousands of variables. We also combine the local search operator with other widely used MOEAs to verify its effectiveness. The experimental results show that the proposed algorithm outperforms MOEAs without the gradient heuristic local search operator. 相似文献
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An improved particle swarm optimization algorithm combined with piecewise linear chaotic map 总被引:3,自引:0,他引:3
Particle swarm optimization (PSO) has gained increasing attention in tackling complex optimization problems. Its further superiority when hybridized with other search techniques is also shown. Chaos, with the properties of ergodicity and stochasticity, is definitely a good candidate, but currently only the well-known logistic map is prevalently used. In this paper, the performance and deficiencies of schemes coupling chaotic search into PSO are analyzed. Then, the piecewise linear chaotic map (PWLCM) is introduced to perform the chaotic search. An improved PSO algorithm combined with PWLCM (PWLCPSO) is proposed subsequently, and experimental results verify its great superiority. 相似文献
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一种改进的禁忌搜索算法及其在连续全局优化中的应用 总被引:2,自引:1,他引:1
禁忌搜索算法是一种元启发式的全局优化算法,是局部搜索算法的一种推广,已被成功地应用于许多组合优化问题中。本文针对有界闭区域上的连续函数全局优化问题,提出了一种改进的禁忌搜索算法,并进行了理论分析和数值实验。数值实验表明,对于连续函数全局优化问题的求解该算法是可行有效的,并且结构简单,迭代次数较少,是一种较好的全局启发式优化算法。 相似文献
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张安玲 《数学的实践与认识》2014,(22)
针对粒子群算法局部搜索能力差,后期收敛速度慢等缺点,提出了一种改进的粒子群算法,该算法是在粒子群算法后期加入拟牛顿方法,充分发挥了粒子群算法的全局搜索性和拟牛顿法的局部精细搜索性,从而克服了粒子群算法的不足,把超越方程转化为函数优化的问题,利用该算法求解,数值实验结果表明,算法有较高的收敛速度和求解精度。 相似文献
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Traditionally, minimum cost transshipment problems have been simplified as linear cost problems, which are not practical in
real applications. Some advanced local search algorithms have been developed to solve concave cost bipartite network problems.
These have been found to be more effective than the traditional linear approximation methods and local search methods. Recently,
a genetic algorithm and an ant colony system algorithm were employed to develop two global search algorithms for solving concave
cost transshipment problems. These two global search algorithms were found to be more effective than the advanced local search
algorithms for solving concave cost transshipment problems. Although the particle swarm optimization algorithm has been used
to obtain good results in many applications, to the best of our knowledge, it has not yet been applied in minimum concave
cost network flow problems. Thus, in this study, we employ an arc-based particle swarm optimization algorithm, coupled with
some genetic algorithm and threshold accepting method techniques, as well as concave cost network heuristics, to develop a
hybrid global search algorithm for efficiently solving minimum cost network flow problems with concave arc costs. The proposed
algorithm is evaluated by solving several randomly generated network flow problems. The results indicate that the proposed
algorithm is more effective than several other recently designed methods, such as local search algorithms, genetic algorithms
and ant colony system algorithms, for solving minimum cost network flow problems with concave arc costs. 相似文献
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《Communications in Nonlinear Science & Numerical Simulation》2010,15(11):3316-3331
This paper describes two new harmony search (HS) meta-heuristic algorithms for engineering optimization problems with continuous design variables. The key difference between these algorithms and traditional (HS) method is in the way of adjusting bandwidth (bw). bw is very important factor for the high efficiency of the harmony search algorithms and can be potentially useful in adjusting convergence rate of algorithms to optimal solution. First algorithm, proposed harmony search (PHS), introduces a new definition of bandwidth (bw). Second algorithm, improving proposed harmony search (IPHS) employs to enhance accuracy and convergence rate of PHS algorithm. In IPHS, non-uniform mutation operation is introduced which is combination of Yang bandwidth and PHS bandwidth. Various engineering optimization problems, including mathematical function minimization problems and structural engineering optimization problems, are presented to demonstrate the effectiveness and robustness of these algorithms. In all cases, the solutions obtained using IPHS are in agreement or better than those obtained from other methods. 相似文献
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整数规划的布谷鸟算法 总被引:1,自引:0,他引:1
布谷鸟搜索算法是一种新型的智能优化算法.本文采用截断取整的方法将基本布谷鸟搜索算法用于求解整数规划问题.通过对标准测试函数进行仿真实验并与粒子群算法进行比较,结果表明本文所提算法比粒子群算法拥有更好的性能和更强的全局寻优能力,可以作为一种实用方法用于求解整数规划问题. 相似文献