共查询到19条相似文献,搜索用时 109 毫秒
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针对多目标优化问题,设计一种基于量子计算和非支配排序遗传算法相结合的智能算法进行求解,综合量子算法和非支配排序遗传算法的优点,在局部搜索和全局搜索之间进行权衡。混合算法采用量子比特对问题的解进行编码,基于量子旋转门算子、分散交叉算子以及高斯变异算子对种群进行更新。进行局部深入搜索时,用一个解在目标空间中跟理想点的距离来评价该解的优劣;进行全局搜索时,基于非支配排序遗传算法中的有效前沿的划分和解之间的拥挤距离来评价某个解。最后,在经典的测试函数ZDT5上对所提混合算法进行了测试。通过对比分析若干项针对有效解集的评价指标,该混合算法在跟最优有效前沿的逼近程度以及有效解集分布的均匀程度上均优于目前得到广泛应用的非支配排序遗传算法。 相似文献
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研究了广泛存在于物流作业中一类新型的装箱问题,主要特征体现在箱子使用费用是关于装载率的凹函数。为求解问题,提出了一种基于分组编码策略的改进差分进化算法,以避免常规实数和整数编码方法存在放大搜索空间的不足。针对分组编码策略,定制化设计了以促进优秀基因传播为导向的新型变异和交叉操作,另外还嵌入了以物品置换为邻域的自适应局部搜索操作以增强局部搜索能力。对以往文献给出算例在不同凹费用函数下进行测试,实验结果显示所提出的算法明显优于BFD启发式算法,并且较遗传算法也有显著性改进。 相似文献
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基于模矢搜索和遗传算法的混合约束优化算法 总被引:1,自引:0,他引:1
近年,免梯度方法又开始引起大家的注意,由于不需要计算函数的梯度.特别适合用来求解那些无法得到梯度信息或需要花很大计算量才能得到梯度信息的问题.本文构造了一个基于模矢搜索和遗传算法的混合优化算法.在模矢搜索方法的搜索步,用一个类似于遗传算法的方法产生一个有限点集.算法是全局收敛的. 相似文献
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作业车间调度是一类求解困难的组合优化问题,本文在考虑遗传算法早熟收敛问题和禁忌搜索法自适应优点的基础上,将遗传算法和禁忌搜索法相结合,提出了一种基于遗传和禁忌搜索的混合算法,并用实例对该算法进行了仿真研究.结果表明,该算法有很好的收敛精度,是可行的,与传统的算法相比较,有明显的优越性. 相似文献
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多目标规划的一种混合遗传算法 总被引:3,自引:0,他引:3
本文利用遗传算法的全局搜索内能力及直接搜索算法的局部优化能力,提出了一种用于多目标规划的混合遗传算法.与Pareto遗传算法相比.本文提出的算法能提高多目标遗传算法优化搜索效率,并保证了能得到适舍决策者要求的Pareto最优解.最后,理论与实践证明其有有效性. 相似文献
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This paper presents a kind of dynamic genetic algorithm based on a continuous neural network, which is intrinsically the steepest decent method for constrained optimization problems. The proposed algorithm combines the local searching ability of the steepest decent methods with the global searching ability of genetic algorithms. Genetic algorithms are used to decide each initial point of the steepest decent methods so that all the initial points can be searched intelligently. The steepest decent methods are employed to decide the fitness of genetic algorithms so that some good initial points can be selected. The proposed algorithm is motivated theoretically and biologically. It can be used to solve a non-convex optimization problem which is quadratic and even more non-linear. Compared with standard genetic algorithms, it can improve the precision of the solution while decreasing the searching scale. In contrast to the ordinary steepest decent method, it can obtain global sub-optimal solution while lessening the complexity of calculation. 相似文献
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A genetic algorithm for robust schedules in a one-machine environment with ready times and due dates
Computing a schedule for a single machine problem is often difficult, but when the data are uncertain, the problem is much more complicated. In this paper, we modify a genetic algorithm to compute robust schedules when release dates are subject to small variations. Two types of robustness are distinguished: quality robustness or robustness in the objective function space and solution robustness or robustness in the solution space. We show that the modified genetic algorithm can find solutions that are robust with respect to both types of robustness. Moreover, the risk associated with a specific solution can be easily evaluated. The modified genetic algorithm is applied to a just-in-time scheduling problem, a common problem in many industries. 相似文献
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Cheng-Hsiung Chiang Liang-Hsuan Chen 《European Journal of Operational Research》2007,180(3):1231-1244
Availability allocation is required when the manufacturer is obliged to allocate proper availability to various components in order to design an end product to meet specified requirements. This paper proposes a new multi-objective genetic algorithm, namely simulated annealing based multi-objective genetic algorithm (saMOGA), to resolve the availability allocation and optimization problems of a repairable system, specifically a parallel–series system. Compared with a general multi-objective genetic algorithm, the major feature of the saMOGA is that it can accept a poor solution with a small probability in order to enlarge the searching space and avoid the local optimum. The saMOGA aims to determine the optimal decision variables, i.e. failure rates, repair rates, and the number of components in each subsystem, according to multiple objectives, such as system availability, system cost and system net profit. The proposed saMOGA is compared with three other multi-objective genetic algorithms. Computational results showed that the proposed approach could provide higher solution quality and greater computing efficiency. 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
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Konstantin Chakhlevitch Celia A. Glass Natalia V. Shakhlevich 《European Journal of Operational Research》2013
We study a problem of minimising the total number of zeros in the gaps between blocks of consecutive ones in the columns of a binary matrix by permuting its rows. The problem is referred to as the Consecutive Ones Matrix Augmentation Problem, and is known to be NP-hard. An analysis of the structure of an optimal solution allows us to focus on a restricted solution space, and to use an implicit representation for searching the space. We develop an exact solution algorithm, which is linear-time in the number of rows if the number of columns is constant, and two constructive heuristics to tackle instances with an arbitrary number of columns. The heuristics use a novel solution representation based upon row sequencing. In our computational study, all heuristic solutions are either optimal or close to an optimum. One of the heuristics is particularly effective, especially for problems with a large number of rows. 相似文献
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We consider the problem of tree template matching, a type of tree pattern matching, where the tree templates have some of their leaves denoted as “donʼt care”, and propose a solution based on the bottom-up technique. Specifically, we transform the tree pattern matching problem for unranked ordered trees to a string matching problem, by transforming the tree template and the subject tree to strings representing their postfix bar notation, and then propose a table-driven algorithm to solve it. The proposed algorithm is divided into two phases: the preprocessing and the searching phase. The tree template is preprocessed once, and the searching phase can be applied to many subject trees, without the need of preprocessing the tree template again. Although we prove that the space required for preprocessing is exponential in the size of the tree template in the worst case, we show that for a specific class of tree templates, the space required is linear in the size of the tree template. The time for the searching phase is linear in the size of the subject tree in the worst case. Thus, the algorithm is asymptotically optimal when one needs to search for a given tree template, of constant to logarithmic size, in many subject trees. 相似文献
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《European Journal of Operational Research》2005,164(1):79-88
Quadratic programming is concerned with minimizing a convex quadratic function subject to linear inequality constraints. The variables are assumed to be nonnegative. The unique solution of quadratic programming (QP) problem (QPP) exists provided that a feasible region is non-empty (the QP has a feasible space).A method for searching for the solution to a QP is provided on the basis of statistical theory. It is shown that QPP can be reduced to an appropriately formulated least squares (LS) problem (LSP) with equality constraints and nonnegative variables. This approach allows us to obtain a simple algorithm to solve QPP. The applicability of the suggested method is illustrated with numerical examples. 相似文献
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Many space mission planning problems may be formulated as hybrid optimal control problems, i.e. problems that include both
continuous-valued variables and categorical (binary) variables. There may be thousands to millions of possible solutions;
a current practice is to pre-prune the categorical state space to limit the number of possible missions to a number that may
be evaluated via total enumeration. Of course this risks pruning away the optimal solution. The method developed here avoids
the need for pre-pruning by incorporating a new solution approach using nested genetic algorithms; an outer-loop genetic algorithm
that optimizes the categorical variable sequence and an inner-loop genetic algorithm that can use either a shape-based approximation
or a Lambert problem solver to quickly locate near-optimal solutions and return the cost to the outer-loop genetic algorithm.
This solution technique is tested on three asteroid tour missions of increasing complexity and is shown to yield near-optimal,
and possibly optimal, missions in many fewer evaluations than total enumeration would require. 相似文献
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The inverse problems of textile materials design on heat and moisture transfer properties are important and indispensable in applications in the body-clothing-environment system. We present an inverse problem of textile porosity determination (IPTPD) based on a nonlinear heat and moisture transfer model. Adopting the idea of the least-squares, the mathematical formulation of IPTPD is deduced to a regularized optimization problem with collocation method applied. The continuity of the regularized minimization problem is proved. By means of genetic algorithm (GA), the approximate solution of the IPTPD is numerically obtained. To reduce the computational cost, an improved algorithm based on BP neural network with GA is proposed in the numerical simulation. Compared with the direct GA searching, the computational cost is greatly reduced, which presents a similar result. 相似文献