Adaptive genetic algorithm-based design of gamma-graphyne nanoribbon incorporating diamond-shaped segment with high thermoelectric conversion efficiency

The gamma-graphyne nanoribbons(γ-GYNRs) incorporating diamond-shaped segment(DSSs) with excellent thermoelectric properties are systematically investigated by combining nonequilibrium Green’s functions with adaptive genetic algorithm. Our calculations show that the adaptive genetic algorithm is efficient and accurate in the process of identifying structures with excellent thermoelectric performance. In multiple rounds, an average of 476 candidates(only 2.88% of all16512 candidate structures) are calculated to obtain the structures with extremely high thermoelectric conversion efficiency.The room temperature thermoelectric figure of merit(ZT) of the optimal γ-GYNR incorporating DSSs is 1.622, which is about 5.4 times higher than that of pristine γ-GYNR(length 23.693 nm and width 2.660 nm). The significant improvement of thermoelectric performance of the optimal γ-GYNR is mainly attributed to the maximum balance of inhibition of thermal conductance(proactive effect) and reduction of thermal power factor(side effect). Moreover, through exploration of the main variables affecting the genetic algorithm, it is revealed that the efficiency of the genetic algorithm can be improved by optimizing the initial population gene pool, selecting a higher individual retention rate and a lower mutation rate. The results presented in this paper validate the effectiveness of genetic algorithm in accelerating the exploration of γ-GYNRs with high thermoelectric conversion efficiency, and could provide a new development solution for carbon-based thermoelectric materials.

Real-Time Patient-Specific ECG Arrhythmia Detection by Quantum Genetic Algorithm of Least Squares Twin SVM

The automatic detection of cardiac arrhythmias through remote monitoring is still a challenging task since electrocardiograms(ECGs)are easily contaminated by physiological artifacts and external noises,and these morphological characteristics show significant variations for different patients.A fast patient-specific arrhythmia diagnosis classifier scheme is proposed,in which a wavelet adaptive threshold denoising is combined with quantum genetic algorithm(QAG)based on least squares twin support vector machine(LSTSVM).The wavelet adaptive threshold denoising is employed for noise reduction,and then morphological features combined with the timing interval features are extracted to evaluate the classifier.For each patient,an individual and fast classifier will be trained by common and patient-specific training data.Following the recommendations of the Association for the Advancements of Medical Instrumentation(AAMI),experimental results over the MIT-BIH arrhythmia benchmark database demonstrated that our proposed method achieved the average detection accuracy of 98.22%,99.65%and 99.41%for the abnormal,ventricular ectopic beats(VEBs)and supra-VEBs(SVEBs),respectively.Besides the detection accuracy,sensitivity and specificity,our proposed method consumes the less CPU running time compared with the other representative state of the art methods.It can be ported to Android based embedded system,henceforth suitable for a wearable device.

Nonlinear amplitude inversion using a hybrid quantum genetic algorithm and the exact zoeppritz equation

The amplitude versus offset/angle(AVO/AVA)inversion which recovers elastic properties of subsurface media is an essential tool in oil and gas exploration.In general,the exact Zoeppritz equation has a relatively high accuracy in modelling the reflection coefficients.However,amplitude inversion based on it is highly nonlinear,thus,requires nonlinear inversion techniques like the genetic algorithm(GA)which has been widely applied in seismology.The quantum genetic algorithm(QGA)is a variant of the GA that enjoys the advantages of quantum computing,such as qubits and superposition of states.It,however,suffers from limitations in the areas of convergence rate and escaping local minima.To address these shortcomings,in this study,we propose a hybrid quantum genetic algorithm(HQGA)that combines a self-adaptive rotating strategy,and operations of quantum mutation and catastrophe.While the selfadaptive rotating strategy improves the flexibility and efficiency of a quantum rotating gate,the operations of quantum mutation and catastrophe enhance the local and global search abilities,respectively.Using the exact Zoeppritz equation,the HQGA was applied to both synthetic and field seismic data inversion and the results were compared to those of the GA and QGA.A number of the synthetic tests show that the HQGA requires fewer searches to converge to the global solution and the inversion results have generally higher accuracy.The application to field data reveals a good agreement between the inverted parameters and real logs.

Adaptive impedance matching using quantum genetic algorithm

An adaptive technique adopting quantum genetic algorithm (QGA) for antenna impedance tuning is presented. Three examples are given with different types of antenna impedance. The frequency range of the dual standards is from 1.7 to 2.2 GHz. Simulation results show that the proposed tuning technique can achieve good accuracy of impedance matching and load power. The reflection coefficient and VSWR obtained are also very close to their ideal values. Comparison of the proposed QGA tuning method with conventional genetic algorithm based tuning method is also given, which shows that the QGA tuning algorithm is much faster. Moreover, the proposed method can be useful for software defined radio systems using a single antenna for multiple mobile and wireless bands.

Cryptanalysis of TEA Using Quantum-Inspired Genetic Algorithms

The Tiny Encryption Algorithm (TEA) is a Feistel block cipher well known for its simple implementation, small memory footprint, and fast execution speed. In two previous studies, genetic algorithms (GAs) were employed to investigate the randomness of TEA output, based on which distinguishers for TEA could be designed. In this study, we used quan-tum-inspired genetic algorithms (QGAs) in the cryptanalysis of TEA. Quantum chromosomes in QGAs have the advan-tage of containing more information than the binary counterpart of the same length in GAs, and therefore generate a more diverse solution pool. We showed that QGAs could discover distinguishers for reduced cycle TEA that are more efficient than those found by classical GAs in two earlier studies. Furthermore, we applied QGAs to break four-cycle and five-cycle TEAs, a considerably harder problem, which the prior GA approach failed to solve.

Revenue Optimization of Pipelines Construction and Operation Management Based on Quantum Genetic Algorithm and Simulated Annealing Algorithm

For the optimization of pipelines, most researchers are mainly concerned with designing the most reasonable section to meet the requirements of strength and stiffness, and at the same time reduce the cost as much as possible. It is undeniable that they do achieve this goal by using the lowest cost in design phase to achieve maximum benefits. However, for pipelines, the cost and incomes of operation management are far greater than those in design phase. Therefore, the novelty of this paper is to propose an optimization model that considers the costs and incomes of the construction and operation phases, and combines them into one model. By comparing three optimization algorithms (genetic algorithm, quantum genetic algorithm and simulated annealing algorithm), the same optimization problem is solved. Then the most suitable algorithm is selected and the optimal solution is obtained, which provides reference for construction and operation management during the whole life cycle of pipelines.

A Cooperative Evolution of Multiple Operators Based Adaptive Quantum Genetic Algorithm for Network Coding Resources Optimization

In order to optimize the network coding resources in a multicast network, an improved adaptive quantum genetic algorithm (AM-QEA) was proposed. Firstly, the optimization problem was translated into a graph decomposition problem. Then the graph decomposition problem was represented by the binary coding, which can be processed by quantum genetic algorithm. At last, a multiple-operators based adaptive quantum genetic algorithm was proposed to optimize the network coding resources. In the algorithm, the individual fitness evaluation operator and population mutation adjustment operator were employed to solve the shortcomings of common quantum genetic algorithm, such as high convergence rate, easy to fall into local optimal solution and low diversity of the population in later stage. The experimental results under various topologies show that the proposed algorithm has the advantages of high multicast success rate, fast convergence speed and strong global search ability in resolving the network coding resource optimization problems.

Directional Modulation Based on a Quantum Genetic Algorithm for a Multiple-Reflection Model

Directional modulation is one of the hot topics in data security researches.To fulfill the requirements of communication security in wireless environment with multiple paths,this study takes into account the factors of reflections and antenna radiation pattern for directional modulation.Unlike other previous works,a novel multiple-reflection model,which is more realistic and complex than simplified two-ray reflection models,is proposed based on two reflectors.Another focus is a quantum genetic algorithm applied to optimize antenna excitation in a phased directional modulation antenna array.The quantum approach has strengths in convergence speed and the globe searching ability for the complicated model with the large-size antenna array and multiple paths.From this,a phased directional modulation transmission system can be optimized as regards communication safety and improve performance based on the constraint of the pattern of the antenna array.Our work can spur applications of the quantum evolutionary algorithm in directional modulation technology,which is also studied.

NOVEL QUANTUM-INSPIRED GENETIC ALGORITHM BASED ON IMMUNITY

A novel algorithm, the Immune Quantum-inspired Genetic Algorithm (IQGA), is proposed by introducing immune concepts and methods into Quantum-inspired Genetic Algorithm (QGA). With the condition of preserving QGA's advantages, IQGA utilizes the characteristics and knowledge in the pending problems for restraining the repeated and ineffective operations during evolution, so as to improve the algorithm efficiency. The experimental results of the knapsack problem show that the performance of IQGA is superior to the Conventional Genetic Algorithm (CGA), the Immune Genetic Algorithm (IGA) and QGA.

A Self-Adaptive Quantum Genetic Algorithm for Network Flow Vehicle Scheduling Problem

Bicycle sharing scheduling is a complex mathematical optimization problem, and it is challenging to design a general algorithm to solve it well due to the uncertainty of its influencing factors. This paper creatively establishes a new mathematical model to determine the appropriate number of vehicles to be placed at each placement point by calculating the traffic weights of the placement points and optimizes the hyperparameters in the algorithm by adaptive quantum genetic algorithm, and at the same time combines the network flow algorithm in graph theory to calculate the most suitable scheduling scheme for shared bicycles by establishing the minimum cost maximum flow network. Through experimental validation, the network flow-based algorithm proposed in this paper allows for a more convenient calculation of the daily bike-sharing scheduling scheme compared to previous algorithms. An adaptive quantum genetic algorithm optimizes the hyperparameters appearing in the algorithm. The experimental results show that the algorithm achieves good results as the transportation cost is only 1/15th of the GA algorithm and 1/9th of the QGA algorithm.

Real-Code Genetic Algorithm for Ground State Energies of Hydrogenic Donors in GaAs-（Ga,Al）As Quantum Dots

We present a global optimization method, called the real-code genetic algorithm (RGA), to the ground state energies. The proposed method does not require partial derivatives with respect to each variational parameter or solving an eigenequation, so the present method overcomes the major difficulties of the variational method. RGAs also do not require coding and encoding procedures, so the computation time and complexity are reduced. The ground state energies of hydrogenic donors in GaAs-(Ga,Al)As quantum dots have been calculated for a range of the radius of the quantum dot radii of practical interest. They are compared with those obtained by the variational method. The results obtained demonstrate the proposed method is simple, accurate, and easy implement.

RESEARCH OF QUANTUM GENETIC ALGORITH AND ITS APPLICATION IN BLIND SOURCE SEPARATION

This letter proposes two algorithms: a novel Quantum Genetic Algorithm (QGA)based on the improvement of Han's Genetic Quantum Algorithm (GQA) and a new Blind Source Separation (BSS) method based on QGA and Independent Component Analysis (ICA). The simulation result shows that the efficiency of the new BSS method is obviously higher than that of the Conventional Genetic Algorithm (CGA).

An Effective Non-Commutative Encryption Approach with Optimized Genetic Algorithm for Ensuring Data Protection in Cloud Computing

Nowadays,succeeding safe communication and protection-sensitive data from unauthorized access above public networks are the main worries in cloud servers.Hence,to secure both data and keys ensuring secured data storage and access,our proposed work designs a Novel Quantum Key Distribution(QKD)relying upon a non-commutative encryption framework.It makes use of a Novel Quantum Key Distribution approach,which guarantees high level secured data transmission.Along with this,a shared secret is generated using Diffie Hellman(DH)to certify secured key generation at reduced time complexity.Moreover,a non-commutative approach is used,which effectively allows the users to store and access the encrypted data into the cloud server.Also,to prevent data loss or corruption caused by the insiders in the cloud,Optimized Genetic Algorithm(OGA)is utilized,which effectively recovers the data and retrieve it if the missed data without loss.It is then followed with the decryption process as if requested by the user.Thus our proposed framework ensures authentication and paves way for secure data access,with enhanced performance and reduced complexities experienced with the prior works.

基于IQGA的船舶电力系统故障定位方法

为了快速精准定位船舶电力系统故障,争取宝贵的船舶电力抢修时间,提出一种改进的量子遗传算法(improved quantum genetic algorithm,IQGA)。首先,搭建船舶电力系统的数学模型,把故障定位问题转化为求目标函数最优问题;接着,将量子计算引入遗传算法(genetic algorithm,GA)中,采用双链量子比特编码方式,改进量子旋转门的角度更新策略;最后,加入量子非门实现染色体变异操作,增强算法收敛性能。仿真实验结果表明,改进量子遗传算法能够精准定位故障区段,并且较传统算法有着更为显著的收敛性能。

Quantum Algorithm of Imperfect KB Self-organization Pt I: Smart Control-Information-Thermodynamic Bounds

The quantum self-organization algorithm model of wise knowledge base design for intelligent fuzzy controllers with required robust level considered.Background of the model is a new model of quantum inference based on quantum genetic algorithm.Quantum genetic algorithm applied on line for the quantum correlation’s type searching between unknown solutions in quantum superposition of imperfect knowledge bases of intelligent controllers designed on soft computing.Disturbance conditions of analytical information-thermodynamic trade-off interrelations between main control quality measures(as new design laws)discussed in Part I.The smart control design with guaranteed achievement of these trade-off interrelations is main goal for quantum self-organization algorithm of imperfect KB.Sophisticated synergetic quantum information effect in Part I(autonomous robot in unpredicted control situations)and II(swarm robots with imperfect KB exchanging between“master-slaves”)introduced:a new robust smart controller on line designed from responses on unpredicted control situations of any imperfect KB applying quantum hidden information extracted from quantum correlation.Within the toolkit of classical intelligent control,the achievement of the similar synergetic information effect is impossible.Benchmarks of intelligent cognitive robotic control applications considered.

基于QGA的船舶电力系统故障诊断仿真研究

为了及时准确地发现船舶电力系统故障区域或故障元件,在考虑到断路器不同保护时段内动作状态的基础上,构建0-1整数规划的诊断模型,利用量子遗传算法QGA(Quantum Genetic Algorithm)诊断方法对测试系统中的故障算例进行仿真测试.测试结果表明,在处理相同故障算例的前提下,与传统的遗传算法诊断方法相比,量子遗传算法诊断方法的收敛速度快,稳定性强.

一种解决组合优化问题的量子遗传算法QGA

本文在量子变异的基础上 ,提出了一种解决组合优化问题的量子遗传算法QGA ,它融合了遗传量子算法GQA和经典遗传算法的优点 ,只用一个个体就可在很短的时间内搜索到最优解 ,并针对一个典型的组合优化问题——— 0 / 1背包问题进行了对比实验 ,实验结果表明本文所提出的量子遗传算法QGA优于传统遗传算法和遗传量子算法GQA .

矿井突水水源识别的QGA-LSSVM模型

为快速、有效地判别矿井突水水源,根据水化学特征,选取Ca2+,Mg2+,K++Na+,HCO-3,SO2-4,Cl-和总硬度等7个指标的质量浓度(mg/L)作为主要判别依据,并将其作为识别的变量。利用因子分析模型对数据进行属性约简,提取公共因子,减少判别指标之间信息交互,并利用量子遗传算法(QGA)对最小二乘支持向量机(LSSVM)的参数寻优,建立QGA-LSSVM水源识别模型。选取从新庄孜煤矿实测的33组数据作为训练数据,进行模型训练;另用12组数据作为测试数据,用该模型进行预测,并将结果与其他预测模型的结果进行对比。研究表明:利用因子分析模型提取的公共因子包含原变量因素中绝大部分信息,能减少信息冗余,利用QGA优化的LSSVM模型能使结果避免陷入极值,用该模型可有效地预测矿井突水水源类型,其错误预测率为0。