Quantum Genetic Algorithm Research Articles

Simulation of implementable quantum-assisted genetic algorithm

Quantum-assisted algorithms are expected to improve the computing performance of classical computers. A quantum genetic algorithm utilizes the advantages of quantum computation by combining the truncation selection in a classical genetic algorithm with the quantum Grover’s algorithm. The parallelism of evaluation can create global search and reduce the need of crossover and mutation in a conventional genetic algorithm. In this work, we aim to demonstrate and simulate the performance of an implementable quantum-assisted genetic algorithm. The algorithm was tested by using quadratic unconstrained binary optimization (QUBO) for 100 iterations; and the results were compared with those from a classical counterpart for 2000 iterations, where both simulations were performed over 100 repetitions. The results showed that the quantum algorithm converges to the optimal solution faster. While the variance is higher at early stage, it quickly and greatly reduces as the algorithm converges. The histograms of possible solutions consistently exhibits this behavior.

Comprehensive dispatch model of agricultural water resources based on multi-objective quantum genetic algorithm

Comprehensive dispatch model of agricultural water resources based on multi-objective quantum genetic algorithm

Automatic Film Label Acquisition Method Based on Improved Neural Networks Optimized by Mutation Ant Colony Algorithm.

Nowadays, with the constant change of public aesthetic standards, a large number of new types and themes of film programs have emerged. For this reason, this paper proposes an improved neural network optimized by mutation ant colony algorithm for automatic acquisition of film labels, which not only overcomes the disadvantages of traditional neural network, such as difficulty in determining weights, slow convergence speed, and easiness to fall into local minimum, but also makes up for the shortcomings faced by using ant colony algorithm alone through the gradient information of quantum genetic algorithm neural network. The results show that the user similarity judgment is added in the process of calculating the user rating deviation between movies, and the neighbor chooses to add the movie tag weight and rating similarity as the basis for the neighbor selection of the target movie in the process of predicting the target movie rating. Experiments show the effectiveness of the algorithm.

Localization and intensity calibration of partial discharge based on attenuation effect of ultrasonic sound pressure

In the insulation system of power equipment, the partial discharge (PD) of short period does not cause the insulation to produce the penetrating breakdown, however the long-term PD of is one of the important causes of local deterioration, and even breakdown in dielectric. Therefore, it is very important to study the location of PD source and the calibration of discharge intensity. To achieve this, in this paper we take the needle-plate discharge model for example and go through the following steps respectively. Firstly, combined with the positive correlation between the ultrasonic signal and the apparent discharge magnitude in the process of PD, the ultrasonic method to detect partial discharge can be implemented. Then, based on the principle of time difference of arrival method (TDOAM), the accuracy of location is analyzed by using quantum genetic algorithm (QGA), genetic algorithm (GA), simulated annealing algorithm (SAA), particle swarm optimization (PSO) and generalized cross correlation method (GCC), respectively. And thus, starting from the study of the attenuation effect of sound pressure caused by the propagation loss, reflection and refraction of acoustic wave, the calibration model of PD intensity is established for the first time after determining the location of PD source with high precision. Some important findings are extracted from simulations and experimental results. First, the localization algorithm of PD source with high precision is observed. The localization of PD source by means of QGA is the most accurate, with maximum deviation of (0.27 ± 0.13) cm. Comparing with GA, SAA, PSO and GCC, the accuracy of location is improved by 33.57%, 41.51%, 32.11% and 87.26%, respectively. Second, due to the attenuation effect of sound pressure, when the measured voltage amplitude of ultrasonic signal is the same, the apparent discharge magnitude of PD source gradually increases with the test distance increasing. When the test distance is 37.80 cm, the apparent discharge magnitude of PD source is 633.83 pC, which increases by 28.51% compared with 7.00 cm. Moreover, simulation results and measurement results are compared with each other and they are well consistent. The discharge curve almost coincides with the calibration fitting curve of PD source when the test distance is 7.00 cm. Finally, it is concluded that the discharge intensity calibration model of PD source is accurate, which is of great significance in evaluating the extent of insulation damage.

A Novel Approach for Service Function Chain Embedding in Cloud Datacenter Networks

Network function virtualization (NFV) and software defined networking (SDN) have recently introduced together, which show great potential in openness, innovation, flexibility and scalability of the networks. Embedding SDN/NFV-enabled service function chain (SFC) has been considered as an efficient way to achieve the end-to-end communication. Considering the multiple users’ delay diversities oriented to future networks, from the perspective of network operators, determining the placement of virtual network functions (VNFs) and routing the service paths is a challenging problem. However, the traditional solutions (e.g., exact, heuristic, meta-heuristic, game, etc.) based on the SFC dynamic embedding problem (SFC-DMP) follow the strong assumptions as usual, which might limit their applications in dynamic and complex network scenarios. To address this narrow, we are motivated to present a novel approach for handling SFC-DMP in the cloud datacenter networks that simultaneously combines the quantum genetic algorithm (QGA) and fuzzy logic. Simulation results show the efficient performance of our proposal.

PERANCANGAN PENEMPATAN RECLOSER YANG OPTIMUM MENGGUNAKAN METODE QUANTUM GENETIC ALGORITHM DI PENYULANG PALAPA

The need for electricity in Bali is increasing along with the growth of technology and developmentin Bali, so the quality of electricity distribution to customers must also be improved. This study took anexample from the 2018 case, Palapa Feeder used 2 reclosers which had an index value that did not meetthe standard of the reliability index value. When the Palapa Feeder only used LBS, SAIFI value was2.1055 and SAIDI was 7.2960, when the LBS was changed to recloser the SAIFI value was 1.203 andSAIDI was 4.091. When a recloser was added again the SAIFI value became 1.016 and SAIDI became3.4088. The use of 2 reclosers on a feeder is less effective, because if they are placed at a close distance,then there will be two devices work simultaneously when there is a disturbance so that the area which isnot affected by the disturbance will also experience a power outage. Therefore it is necessary to design arecloser placement point in order to obtain lower reliability index value in order to meet the PLN standard(SPLN) No. 68-2: 1986, World Class Company standards, World Class Service standards, and alsoobtained the optimum recloser location and more efficient.The design of recloser placement is carried out using the Quantum Genetic Algorithm method,which is an evolutionary algorithm based on the concept of quantum bits (qubits). The purpose of thismethod is to minimize the SAIDI and SAIFI value in order to obtain lower reliability index and meet thestandards in order to obtain an optimum recloser location. The results of the recloser placement designusing the QGA method show that the local optimum recloser location is in section 2 at point 56 with thehighest fitness value is 4.9715 and a SAIDI value is 1.8951 decreased by 44.4% and SAIFI value is 0.9978decreased by 1, 78%, and it is proven that the recloser placement design using the QGA method hassucceeded in obtaining local optimum with better SAIDI SAIFI index results and has met the PLN (SPLN)standard No. 68-2: 1986 with SAIDI 3,33 and SAIFI 1,2 and also meets World Class Company and WorldClass Service standards with SAIFI 3 (times/customer/year) and SAIDI 1,66 (hours/customer/year).

Design optimization of office building envelope based on quantum genetic algorithm for energy conservation

Designing envelope configurations of building with the low construction cost and low energy consumption is of important significance to green building. At present, there are some mature professional software for building optimization design, however, these programs run for a long time and require detailed input of building parameters, which makes the design very inconvenient. It is relatively convenient to use some optimization algorithms to optimize the design of buildings. Genetic algorithm is a common algorithm for building design optimization. However, genetic algorithm has the disadvantage of being easy to be trapped into local optimization. Based on this, in this paper, we propose a design optimal method of office building envelope based on quantum genetic algorithm. We optimize the office building envelope structure, such as walls, windows, glass curtain wall, numbers of windows, etc. to minimize the construction cost at the required energy conservation. Compared with the traditional genetic algorithm, in this paper, when using the quantum genetic algorithm makes the design optimization of office building envelope configuration at the required ENVLOAD (energy load of building envelope) value, the total window area are increased by 13.8%, which means that the natural ventilation is better; glass curtain wall ratio is increased by 14%, which means that indoor lighting is better; the cost fall by 0.7%, which means the cost is lower; the generation number to achieve convergence is reduced by 50%, which means the convergence is faster. And compared with original design, the total cost reduces by 35.3%. Compared with other literatures, the construction cost per unit area of envelope in this paper is lower and the building energy load per unit area of envelope is smaller.

Application of a novel fractional grey prediction model with time power term to predict the electricity consumption of India and China

Abstract As one of the most important energy sources, electricity plays an important role in power system and is the main driving force for the development of the country and society. Accurately forecasting electricity consumption is of significance of the power system and market. For this, a novel fractional grey polynomial model with time power term (denoted as F P G M ( 1 , 1 , t α ) ) is developed for forecasting electricity consumption of India and China, in which the grey polynomial model is optimized by combining time power term and fractional accumulation, the quantum genetic algorithm (QGA) is then applied to determine the model parameters. Particularly, the proposed model can be changed to other existing models by adjusting systematic coefficient. The numerical results shows that the proposed model outperforms other competitive models. Given the efficacy of the proposed model, it is applied to predict electricity consumption in the coming years, which could provide with a reference in preparing energy policies and strategies.

Application of a quantum genetic algorithm and QTAIM analysis in the study of structural and electronic properties of neutral bimetallic clusters NaxLiy (4 ≤ x + y ≤ 10).

Alloy clusters of NaxLiy (4 ≤ x + y ≤ 10) are studied by exploring the potential energy surface in the ab initio MP2 level with the support of a quantum genetic algorithm (QGA). In some cases, the structures have been also refined with DFT and coupled-cluster methods. The general trends of sodium-lithium structures are in line with previous studies. The ionization potentials and polarizabilities to all structures were calculated with MP2 method and the average error between these two properties compared with experimental data was 6% and 13%, respectively. The topological analysis based on quantum theory of atoms in molecules (QTAIM) showed that by increasing the cluster size of the diatomic system there was a decrease of atomic interaction energies. The degree of degeneracy from D3BIA aromaticity index and the analysis of the atomic charges showed the influence (by charge transfer) of the chemical element in lower quantity in the cluster with respect to the other atoms. Our achievements of comparing our theoretical results with available experimental data have demonstrated that our approach can also predict satisfactorily quantum atomic and alloy clusters properties, at least, for low nuclearities.

Self-calibration for linear structured light 3D measurement system based on quantum genetic algorithm and feature matching

In this paper, a self-calibration method for a linear structured light 3D measurement system and its advantages are presented. According to the mathematical model of the linear-structured light 3D measurement system, the calibration problem is a highdimensional optimization problem. The principle of this self-calibration method can be drawn from a traditional calibration method. In this method, quantum genetic algorithm and feature matching are applied to self-calibration. Feature matching is used to derive two points with fixed spatial relationships, and an optimal solution of system parameters can be obtained by quantum genetic algorithm. Finally, experimental results are given. The measurement error is 0.05 mm and the ratio of the error to scanning distance is 1.54e-4.In this paper, a self-calibration method for a linear structured light 3D measurement system and its advantages are presented. According to the mathematical model of the linear-structured light 3D measurement system, the calibration problem is a high-dimensional optimization problem. The principle of this self-calibration method can be drawn from a traditional calibration method. In this method, quantum genetic algorithm and feature matching are applied to selfcalibration. Feature matching is used to derive two points with fixed spatial relationships, and an optimal solution of system parameters can be obtained by quantum genetic algorithm. Finally, experimental results are given. The measurement error is 0.05 mm and the ratio of the error to scanning distance is 1.54e-4.

Optimization of Deterministic Pilot Pattern Placement Based on Quantum Genetic Algorithm for Sparse Channel Estimation in OFDM Systems

Optimization of Deterministic Pilot Pattern Placement Based on Quantum Genetic Algorithm for Sparse Channel Estimation in OFDM Systems

Inverse Analysis of the Partitioning Deformation Modulusof High-Arch Dams Based on Quantum Genetic Algorithm

The physical and mechanical parameters of hydraulic structures in complicated operating conditions often change over time. Updating these parameters in a timely manner is important to comprehend the operating behaviors and monitor the safety of hydraulic structures. Conventional inverse analysis methods can only generate inversions on the comprehensive deformation modulus of concrete dam structures, which contradict practical conditions. Based on the researches on conventional reversion methods of the deformation modulus of the dam body, foundation, and reservoir basin, the objective fitness function is established in this paper according to engineering-measured data and finite element simulation results. The quantum genetic algorithm has high global search efficiency and population diversity. A mechanical parameter inversion of high-arch dams is built from the intelligent optimization of an established algorithm by applying the quantum genetic algorithm. The proposed algorithm is tested to be feasible and valid for practical engineering projects and therefore shows scientific and practical application values.

Event‐triggered load frequency control for multi‐area power systems based on Markov model: a global sliding mode control approach

In this study, a new method is put forward for the stability and stabilisation analysis of the event-triggered load frequency control (LFC) with interval time-varying delays, considering the global sliding mode controller. To lighten the network bandwidth and save more limited networked resources, the event-triggered scheme is optimised through quantum genetic algorithm, according to different circumstances. Additionally, global sliding mode control (GSMC) scheme is proposed to provide stronger robustness performance, which against the frequency deviation caused by power unbalance or transmission time delays better. Based on the proposed schemes, multi-area LFC for the power system model is formulated as a Markov jump linear system model, considering transmission time delays and external disturbances. By applying improved Lyapunov stability theory, criteria about the stability and stabilisation conditions for multi-area power system can be deduced in terms of linear matrix inequality. Finally, to validate a more realistic LFC application, the proposed event-triggered GSMC is also deployed on Kundur's two-area test system. Simulation studies are carried out to illustrate the effectiveness and superiority of the developed schemes.

Quantum and classical genetic algorithms for multilevel segmentation of medical images: A comparative study

In this paper, we propose a multilevel segmentation methods of medical images based on the classical and quantum genetic algorithms. The Genetic Algorithm (GA) uses a binary coding while the Quantum Genetic Algorithm (QGA) uses the qubit encoding of individuals. The two evolutionary algorithms are employed to maximize efficiently Rényi, Masi and Shannon entropies for the purpose of multi-objects segmentation of medical images. The Particle Swarm Optimization algorithm (PSO) was also used for comparison reasons. The segmentation quality of the nine proposed approaches is assessed by means of the prevailing indices PSNR, SSIM and FSIM. The numerical results and the comparative study were carried out on a sample of twenty medical images. It was shown that the QGA outpaces the GA, and the PSO outperforms significantly the both algorithms in the optimization task. Finally, it was found that the Rényi entropy is more suitable for the purpose of medical image multilevel thresholding.

Improved adaptive impedance matching for RF front-end systems of wireless transceivers

In this paper an automatic adaptive antenna impedance tuning algorithm is presented that is based on quantum inspired genetic optimization technique. The proposed automatic quantum genetic algorithm (AQGA) is used to find the optimum solution for a low-pass passive T-impedance matching LC-network inserted between an RF transceiver and its antenna. Results of the AQGA tuning method are presented for applications across 1.4 to 5 GHz (satellite services, LTE networks, radar systems, and WiFi bands). Compared to existing genetic algorithm-based tuning techniques the proposed algorithm converges much faster to provide a solution. At 1.4, 2.3, 3.4, 4.0, and 5.0 GHz bands the proposed AQGA is on average 75%, 49.2%, 64.9%, 54.7%, and 52.5% faster than conventional genetic algorithms, respectively. The results reveal the proposed AQGA is feasible for real-time application in RF-front-end systems.

A novel hyper-chaotic image encryption scheme based on quantum genetic algorithm and compressive sensing

Over the last few years, lots of chaotic image encryption schemes have been proposed. However, most of the schemes are permutation-diffusion architectures which still have some shortcomings, such as weak key streams, small key spaces, small information entropy, and so on. To eliminate the above weaknesses, in this paper, we propose a hyper-chaotic image encryption scheme based on quantum genetic algorithm (QGA) and compressive sensing (CS), which is a new image encryption scheme and has not been proposed so far. Firstly, QGA can update the population with the quantum rotation gate, which can enhance the randomness of the population and avoid falling into local optimum. Then compressive sensing technology is used to reduce data storage and speed up the encryption and decryption process. Moreover, we utilize the SHA-512 hash function of the plain image to calculate the initial values of the hyper-chaotic system, which is capable of enhancing the relationships between encryption schemes and plain images. The simulation experiments and security analysis reveal that the proposed scheme is more efficient in resisting statistical attack and plaintext attack and shows better performance in peak signal-to-noise ratio (PSNR) and information entropy compared with other image encryption schemes based on chaos theory.

Research on capacity planning and optimization of regional integrated energy system based on hybrid energy storage system

Some regional integrated energy systems (RIES) have installed equipments such as wind turbine and photovoltaic, but the fluctuation of these intermittent power supply is large, resulting in a certain amount of energy waste. Because energy storage can improve the utilization rate of renewable energy, this paper establishes a storage capacity expansion planning model considering multiple functions of hybrid energy storage in the RIES. In this model, the wavelet packet decomposition method is used to reasonably decompose the high-frequency fluctuation and low-frequency fluctuation of renewable energy to achieve the function of stabilizing fluctuation. Meanwhile, an operation strategy for hybrid energy storage to participate in demand response is proposed. Rain flow counting method is used to research the life of hybrid energy storage system, which improves the accuracy of energy storage annual cost calculation. In the Simulation and Analysis section, the genetic quantum algorithm is used for simulation and analysis of the role and benefits of hybrid energy storage. The simulation results show that the benefit of hybrid energy storage in capacity expansion construction is increased by 10.4%, and when the electricity and gas prices fluctuate by ±20%, the hybrid energy storage configuration proposed in this paper has a stable advantage in more than 95%.

Application of a novel grey forecasting model with time power term to predict China's GDP

PurposeGross domestic product (GDP) is an important indicator to measure a country's economic development. If the future development trend of a country's GDP can be accurately predicted, it will have a positive effect on the formulation and implementation of the country's future economic development policies. In order to explore the future development trend of China's GDP, the purpose of this paper is to establish a new grey forecasting model with time power term to forecast GDP.Design/methodology/approachFirstly, the shortcomings of the traditional grey prediction model with time power term are found out through analysis, and then the generalized grey prediction model with time power term is established (abbreviated as PTGM (1,1, α) model). Secondly, the PTGM (1,1, α) model is improved by linear interpolation method, and the optimized PTGM (1,1, α) model is established (abbreviated as OPTGM (1,1, α) model), and the parameters of the OPTGM (1,1, α) model are solved by the quantum genetic algorithm. Thirdly, the advantage of the OPTGM (1,1, α) model over the traditional grey models is illustrated by two real cases. Finally the OPTGM (1,1, α) model is used to predict China's GDP from 2020 to 2029.FindingsThe OPTGM (1,1, α) model is more suitable for predicting China's GDP than other grey prediction models.Originality/valueA new grey prediction model with time power term is proposed.

Optimal design of load frequency active disturbance rejection control via double-chains quantum genetic algorithm

This paper tackles the design of active disturbance rejection controllers (ADRC) for load frequency control (LFC) of multi-area interconnected power systems. For the first time, the double-chains quantum genetic algorithm plays a role in tuning optimization of the parameters for ADRC. Not only is the proposed approach applied to the two-area reheat thermal power system, but it is also elongated to two-interconnected multi-source areas comprising thermal, hydro and gas units, two-area nonlinear non-reheat thermal power system with governor dead band in addition to three-area nonlinear reheat thermal power system with generation rate constraints. Comparison with other modern heuristic optimization strategies recently published proves the effectiveness and superiority of this method. The simulation results show that this robust approach can greatly shorten the stabilization time of the power system and meet the requirements of LFC with minimum transient deviation as well as steady-state performance indicators, which is worthy of application and promotion.

Smart City Moving Target Tracking Algorithm Based on Quantum Genetic and Particle Filter

In the application of moving target tracking in smart city, particle filter technology has the advantages of dealing with nonlinear and non-Gaussian problems, but when the standard particle filter uses resampling method to solve the degradation phenomenon, simply copying the particles will cause local optimization difficulties, resulting in unstable filtering accuracy. In this paper, a particle filter algorithm combined with quantum genetic algorithm (QGA) is proposed to solve the above problems. Aiming at the problem of particle exhaustion in particle filter, the algorithm adopts the method of combining evolutionary algorithm. Each particle in particle filter is regarded as a chromosome in genetic algorithm, and the fitness of each chromosome corresponds to the weight of particle. For each particle state with weight, the particle is first binary coded with qubit and quantum superposition state, and then quantum rotation gate is used for selection, crossing, mutation, and other operations, after a set number of iterations, the final particle set with accuracy and better diversity. In this paper, the filter state estimation and RMSF of N=50 and N=100 for nonlinear target tracking and the comparison of real state and state estimation trajectory in time-constant model under nonlinear target tracking are given. It can be seen that in nonlinear state, the quantum genetic and particle filter (QGPF) algorithm can achieve a higher accuracy of state estimation, and the filtering error of QGPF algorithm at each time is relatively uniform, which shows that the algorithm in this paper has better algorithm stability. Under the time-constant model, the algorithm fits the real state and realizes stable and accurate tracking.