排序方式: 共有72条查询结果,搜索用时 15 毫秒
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二次背包问题是一种NP难组合优化问题,其精确算法求解难度大,针对该问题提出了一种量子进化算法求解方法。该算法采用一种相对贪婪修补算子,该修补算子不但考虑了二次背包问题的每一物品项价值,而且考虑了物品的协作价值,是一种动态修补算子。同时算法借鉴粒子群算法中粒子的运动方程,提出了一种具有三类知识学习能力的量子更新模式,使得量子进化中获得的知识更全面。通过对100个国际上大规模二次背包问题进行测试实验,验证了提出的求解算法比相应的其他启发式算法性能有较大提升。 相似文献
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在传统图像中值滤波算法中,固定排序窗和无条件中值运算会影响算法的降噪能力。为此,提出一种改进的图像中值滤波算法。借鉴量子理论提出数字图像的伪量子化表示形式,应用量子哈达玛变换引入自适应机制,使排序窗口的大小和形状能根据其移动位置的图像局部特征自适应地变化,并引入有条件中值运算保留图像细节。仿真结果表明,与传统中值滤波和递归中值滤波算法相比,该算法在保留图像细节的同时,具有更强的降噪能力,且噪声强度对滤波效果的影响较小。 相似文献
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This paper studied two-stage permutation flow shop problems with batch processing machines, considering different job sizes and arbitrary arrival times, with the optimisation objective of minimising the makespan. The quantum-inspired ant colony optimisation (QIACO) algorithm was proposed to solve the problem. In the QIACO algorithm, the ants are divided into two groups: one group selects the largest job in terms of job size as the initial job for each batch and the other group selects the smallest job as the initial job for each batch. Each group of ants has its own pheromone matrix. In the computational experiment, our novel algorithm was compared with the hybrid discrete differential evolution (HDDE) algorithm and the batch-based hybrid ant colony optimisation (BHACO) algorithm. Although the HDDE algorithm has a shorter run time, the quality of the solution for large-scale jobs is not good, while the BHACO algorithm always obtains a better solution but requires a longer run time. The computational results show that the QIACO algorithm embedded in the quantum information has advantages in terms of both solution quality and running time. 相似文献
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针对硅通孔(through-silicon-via,TSV)的生产成本高,占用面积大等问题,首先对三维片上网络(3D NoC)进行测试规划研究,将测试规划得到的最短测试时间作为约束条件,采用改进的量子进化算法优化测试占用的TSV数量,将各层的TSV按照需求进行配置,并将TSV合理有效地分配给各个内核,以在有限的TSV数量下,降低硬件开销,提高利用率,同时,探讨TSV的分配对测试时间的影响。算法中,引入量子旋转门旋转角动态调整策略和量子变异策略,以提高算法的全局寻优能力和收敛速度,避免陷入局部最优解。将ITC’02基准电路作为仿真实验对象,由实验结果可得,本算法能够快速地收敛到最佳解,有效的减小了测试时间,优化了TSV数量,提高了TSV的利用率。 相似文献
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测试优化选择是复杂电子系统测试性设计中的一个重要问题.首先从测试容差的角度分析了测试发生漏检和虚警的原因,在此基础上建立了测试不可靠条件下一种新的测试选择模型,模型以测试代价、漏检代价和虚警代价之和最小为优化目标,以故障检测率和故障隔离率为约束条件;然后提出一种改进的量子进化算法对模型求解,该算法通过改进一种已有可靠测试选择算法而成,包括种群初始化、适应度计算和种群的进化策略.最后通过两个仿真实例验证了求解算法及模型的有效性和优越性. 相似文献
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Gwo-Ruey Yu Yu-Chia Huang Chih-Yung Cheng 《International journal of systems science》2016,47(9):2225-2236
In the field of fuzzy control, control gains are obtained by solving stabilisation conditions in linear-matrix-inequality-based Takagi–Sugeno fuzzy control method and sum-of-squares-based polynomial fuzzy control method. However, the optimal performance requirements are not considered under those stabilisation conditions. In order to handle specific performance problems, this paper proposes a novel design procedure with regard to polynomial fuzzy controllers using quantum-inspired evolutionary algorithms. The first contribution of this paper is a combination of polynomial fuzzy control and quantum-inspired evolutionary algorithms to undertake an optimal performance controller design. The second contribution is the proposed stability condition derived from the polynomial Lyapunov function. The proposed design approach is dissimilar to the traditional approach, in which control gains are obtained by solving the stabilisation conditions. The first step of the controller design uses the quantum-inspired evolutionary algorithms to determine the control gains with the best performance. Then, the stability of the closed-loop system is analysed under the proposed stability conditions. To illustrate effectiveness and validity, the problem of balancing and the up-swing of an inverted pendulum on a cart is used. 相似文献
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