Parallel Quantum Genetic Algorithm Research Articles

Intrusion detection using rough-fuzzy set and parallel quantum genetic algorithm

An intrusion detection method using rough-fuzzy set and parallel quantum genetic algorithm (RFS-QGAID) is proposed in this paper. The RFS-QGAID is applied to solve the serious problems of determining the optimal antibodies subsets used to detect an anomaly. To obtain a simplified antibodies collection for high dimensional Log data sets, RFS is applied to delete the redundant antibody features and obtain the optimal antibodies features combination. Then, the optimal attitudes are entered into the QGA classifier for learning and training in the following stage. At last, the detected Log antigens are fed into RFS-QGAID, and we can classify the intrusion types. With RFS-QGAID, we give the simulations, the results on real Log data sets show that: the higher detection accuracy of RFS-QGAID is higher detection accuracy, but the false negative rate is lower for small samples sets, the adaptive performance is higher than other detection algorithms.

Dynamic Economic Dispatching Considering Time-Coupling Spinning Reserve Response Risk with High Penetration of Wind Power

Aiming at the problem that the current dynamic economic dispatch (DED) fails to consider the response risk of spinning reserve caused by the fluctuation and uncertainty of wind power, we work out a DED problem considering time-coupling spinning reserve response risk while the stochasticity and variability arising from RESs are taken into consideration. The developed framwork unified the response risk of reserve caused by forced shutdown of the unit into the response risk caused by time coupling. The expected customer interruption cost (ECOST) and the expected abandoned wind cost considering this reserve response risk are added to the objective function. While seeking the minimum objective function, the system is automatically configured with suitable reserve to ensure the consistency of the system’s response risk in each period. An improved multi-universe parallel quantum genetic algorithm was used to solve the model. Numerical examples and analysis prove the effectiveness and feasibility of the proposed method.

Intrusion Detection Using Normalized Mutual Information Feature Selection and Parallel Quantum Genetic Algorithm

This paper presents a detection algorithm using normalized mutual information feature selection and cooperative evolution of multiple operators based on adaptive parallel quantum genetic algorithm (NMIFS MOP- AQGA). The proposed algorithm is to address the problems that the intrusion detection system (IDS) has lower the detection speed, less adaptability and lower detection accuracy. In order to achieve an effective reduction for high-dimensional feature data, the NMIFS method is used to select the best feature combination. The best features are sent to the MOP- AQGA classifier for learning and training, and the intrusion detectors are obtained. The data are fed into the detection algorithm to ultimately generate accurate detection results. The experimental results on real abnormal data demonstrate that the NMIFS MOP- AQGA method has higher detection accuracy, lower false negative rate and higher adaptive performance than the existing detection methods, especially for small samples sets.

Coarse grained parallel quantum genetic algorithm for reconfiguration and service restoration of electric power networks

In this paper, a Coarse Grained Parallel Quantum Genetic Algorithm (CGPQGA) is proposed to solve the network reconfiguration and restoration problems in distribution networks with the objective of reducing network losses, balancing load and improving the quality of voltage in the system. Based on t he parallel evolutionary concept and the insights of quantum theory, a model of parallel quantum computation was simulated. In this frame, there are some demes (sub-populations) and some universes (groups of populations), which are structured in super star-shaped topologies. A new migration scheme based on penetration theory is developed to control both the migration rate and direction adaptively between demes and a coarse grained quantum crossover strategy is devised among universes. The proposed approach is tested on 33-bus distribution networks in two cases the first case without distributed generators and the second case with distributed generators, with the aim of minimizing the losses of reconfigured network, and minimize number of operated switches, deviation of bus voltages, and loading of transformer (power loss) where the choice of the switches to be opened is based on the calculation of voltages at the system buses, real and reactive power flow through lines, real power losses and voltage deviations, using distribution load flow (DLF) program. Simulation results prove the effectiveness of the proposed methodology in solving the current challenges in this phase.

A Nonlinear Blind Source Separation Method Based On Radial Basis Function and Quantum Genetic Algorithm

Blind source separation is a hot topic in signal processing. Most existing works focus on dealing with linear combined signals, while in practice we always encounter with nonlinear mixed signals. To address the problem of nonlinear source separation, in this paper we propose a novel algorithm using radial basis function neutral network, optimized by multi-universe parallel quantum genetic algorithm. Experiments show the efficiency of the proposed method.

Quantum genetic algorithm for adaptive image multi-thresholding segmentation

An adaptive image multilevel thresholding segmentation algorithm is presented in this paper. The proposed algorithm introduces a parallel quantum genetic algorithm PQGA for histogram-based image segmentation. Quantum genetic algorithm QGA has the advantages of fast convergence speed and strong global search capabilities. And PQGA can improve the computational efficiency of the QGA further. Without predetermining the number of the thresholds, the proposed algorithm that chooses the automatic thresholding criterion as its objective function can obtain the number of the thresholds and the corresponding thresholds accurately. The experimental results demonstrate good performance of the PQGA in solving adaptive multilevel thresholding segmentation problems by comparing with other methods for several test images.

Parallel Quantum Genetic Algorithm Using Sphere Multi-region Division

摘要: 论文提出一种基于球面解空间划分的量子遗传算法，引入多区域并行搜索的机制，制定了群间的染色体置换策略，设计了新的量子变异操作，并以种群退化的程度来确定变异的概率。通过理论分析证明了该算法能够以概率1收敛到全局最优解。在组合优化和连续优化问题的实验中，该算法能够以较快的速率收敛到目标值，收敛过程相对平稳，降低了早熟现象产生的概率，表现出了良好的性能。 关键词: 量子遗传算法 / 多区域划分 / 并行搜索 / 染色体置换 / 量子变异

A novel parallel quantum genetic algorithm for stochastic job shop scheduling

In this paper, a Novel Parallel Quantum Genetic Algorithm (NPQGA) is proposed for the stochastic Job Shop Scheduling Problem with the objective of minimizing the expected value of makespan, where the processing times are subjected to independent normal distributions. Based on the parallel evolutionary idea and some concepts of quantum theory, we simulate a model of parallel quantum computation. In this frame, there are some demes (sub-populations) and some universes (groups of populations), which are structured in super star-shaped topologies. A new migration scheme based on penetration theory is developed to control migration rate and direction adaptively between demes, and a novel quantum crossover strategy is devised among universes. The quantum evolution is executed in every deme by applying some improvement operators (the coding mechanism aiming at job shop, the new quantum rotation angle and the catastrophe operator). Experiment results show NPQGA's effectiveness and applicability.