Quantum Genetic Algorithm Research Articles

A New Fog-Based Transmission Scheduler on the Internet of Multimedia Things Using a Fuzzy-Based Quantum Genetic Algorithm

The Internet of Multimedia Things (IoMT) has recently experienced a considerable surge in multimedia-based services. Due to the fast proliferation and transfer of massive data, the IoMT has service quality challenges. This paper proposes a novel fog-based multimedia transmission scheme for IoMT using the Sugano interference system with a quantum genetic optimization algorithm. The fuzzy system devises a mathematically organized strategy for generating fuzzy rules from input and output variables. The Quantum Genetic Algorithm (QGA) is a metaheuristic algorithm that combines genetic algorithms and quantum computing theory. It combines many critical elements of quantum computing, such as quantum superposition and entanglement. This provides a robust representation of population diversity and the capacity to achieve rapid convergence and high accuracy. As a result of the simulations and computational analysis, the proposed fuzzy-based QGA scheme improves packet delivery ratio and throughput by reducing end-to-end latency and delay when compared to traditional algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Heterogeneous Earliest-Finish-Time (HEFT) and Ant Colony Optimization (ACO). Consequently, it provides a more efficient scheme for multimedia transmission in IoMT.

A Modified Aeromagnetic Compensation Method Robust to the Motion State of Aviation Platform Part

Aeromagnetic compensation is a signal-processing method to suppress the magnetic interference from the airborne platform that far exceeds the target magnetic signal. With the development of measurement technology, the traditional aeromagnetic compensation model in which the motion platform is considered as a rigidly connected whole cannot satisfy the request for high-quality aeromagnetic detection, and the magnetic interference generated by the small relative motion components needs to draw our attention. In this paper, the source of maneuver magnetic interference on the aircraft platform is split into two parts, i.e., the rigidly connected main body part (MBP) and moving part (MP), and their compensation models can be both built by referring to the Tolles–Lawson (TL) model. The key factor in these two models is how to obtain the direction cosine that describe the maneuvering actions of aircraft. In the MBP compensation model, the direction cosine can be directly obtained by the onboard fluxgate magnetometer. However, those in the MP model cannot be directly obtained because there is no any monitoring equipment for it. Therefore, a scheme for calculating the direction cosine of the MP compensation model is proposed. This scheme combines the direction cosine obtained by the onboard fluxgate magnetometer with the relative motion angles. And considering the influence of geomagnetic gradient magnetic interference, its compensation model is brought to form a new aeromagnetic compensation model, herein called the TLGM model. This model is different from the traditional TL model and is nonlinear since the three unknown relative motion angles, so the quantum genetic algorithm (QGA) is introduced to combine with recursive least-squares to estimate those unknowns. Some field experiments were conducted, and the results show that the proposed method reduces the standard deviations and figures of merit by over 42% and 39%, respectively, compared with those using the previous model. Also, the proposed compensation method requires no additional motion monitoring sensors and is more convenient for practical applications.

Research on the balance optimization of investment demand and investment capability of power grid enterprises

In the context of the in-depth advancement of the power system reform and the increasingly stringent supervision of state-owned assets and state-owned enterprises, it is urgent to clarify the balance between investment demand and investment capacity of the power grid. First, the investment capacity and investment demand estimation models are established respectively, and then a dynamic balance optimization model between investment capacity and investment demand is established. By adjusting the range of adjustable indicators of the investment ability and demand calculation model, the objective is set to be the smallest difference between the investment ability and the investment need, the Quantum Genetic Algorithm is used to solve the problem. And consider future development scenarios to achieve a dynamic balance between investment ability and the investment need. Through the analysis of calculation examples, it is known that the investment capacity of a certain place is 630 million yuan, and the investment demand is 661 million yuan. Combined with the development of related factors affecting the investment in the future, when the relevant factors are at a certain value, the two reach a balance, and the investment scale after the balance is 649 million yuan, which verifies the feasibility of investment optimization of the investment scale, so as to support the investment decision of power grid.

Energies of the ground and excited states of confined two-electron atom in finite potential well

For the ground and excited configurations, we have carried out the energy eigenvalues and corresponding wavefunctions of two-electron atom enclosed by finite confining potential by using Quantum Genetic Algorithm (QGA) procedure and Hartree-Fock Roothaan (HFR) method. Besides, we have analysed in detail orbital energies and probabilities of finding electron for the configurations 1S (1s2), 3S (1s2s), 3P (1s2p), 3D (1s3d) and 3F (1s4f). The results reveal that the potential barrier height plays a significant role on average energies, state energies, orbital energies and probabilities of finding electrons in orbitals. As dot size is extremely small and extremely large, the energies for 1snl configurations are getting close to each other, but between these two extreme situations they are separating from each other. While going to higher excited states, singlet and triplet states are almost the same due to the weakening of the exchange energy.

Quantum algorithms: applications, criteria and metrics

In the field of data processing and IoT communication it is possible to develop more robust solutions by combining quantum algorithms with metaheuristics. Said solutions can be applied in the industry and be measured using metrics associated with complexity, efficiency, processing, and accuracy. An extensive bibliographical review is carried out to determine which is the most efficient and effective hybrid algorithm that can be applied to a real experimental case, which aims to improve communication to reduce occupational risks. Criteria, metrics, and experimental results were obtained, in which it is shown that the quantum genetic algorithm is better than the genetic algorithm. A detailed discussion on the objective function, the convergence to the global optimum, and the need to improve the obtained solutions is given. The conclusions raise new aspects that need investigation.

Quantum Genetic Algorithm for the Design of Miniaturized and Reconfigurable IoT Antennas

This paper leverages an automated quantum genetic algorithm (QGA) to design miniaturized antennas for Internet of Things (IoT) devices. The QGA implemented in this work automates the design process and enables the antenna designer to generate static or reconfigurable miniaturized microstrip folded antenna topologies. The QGA is also implemented to generate a pixelation pattern within the antenna radiating surface to optimally tune the antenna operation for IoT communication needs as well as for Wi-Fi operation. In this paper, we also present a QGA-optimized folded miniaturized antenna in both static and reconfigurable topologies. The reconfiguration is achieved through a single RF MEMS whose location is optimized through the automated process. The automated design process achieves 87% antenna size reduction at 868 MHz compared to a typical patch antenna operating at the same frequency. As a result, the static and reconfigurable antenna prototypes achieve good radiation efficiencies in comparison to their miniature size with acceptable gains thus satisfying the IoT physical and operational constraints. Both antennas are fabricated and tested, where excellent agreement is noticed between simulated and measured results.

Disassembly Sequence Planning of Product Structure With an Improved QICA Considering Expert Consensus for Remanufacturing

Planning a disassembly sequence of product structure in a remanufacturing-oriented manner is a newly emerging and important problem, which has attracted considerable attention owing to high demand for sustainable development. However, current approaches cannot lead to a reliable disassembly plan focused on the product structure itself, starting from functional properties to application demand stimulated by remanufacturing. First, they fail to focus on the role that end-of-life (EOL) products play in the promotion of remanufacturing. Second, determining a disassembly solution for advanced and sophisticated products is complicated and time-consuming. Third, the uncertainty of mixed information triggered by the intercrossing of multiple subjects is neglected. In this study, an improved disassembly sequence planning (DSP) method is firstly proposed by integrating the remanufacturing properties of EOL products, an improved quantum immune clonal algorithm (QICA), and the measure and update of the reliability of experts' decision-making to perform DSP. The functional recoverability of EOL product parts is marked to identify the remanufacturing value of a disassembly plan. The expected reproduction probability is adopted to increase population diversity and improve the ability of QICA to search for feasible solutions. The model and update mechanism of experts' recognition degree are constructed to narrow the score deviation. Finally, we provide a case for a sewing machine to verify the superiority of the improved QICA. The results show that it is capable of obtaining satisfactory solutions and outperforms the quantum genetic algorithm and particle swarm optimizer.

A novel coal-gangue recognition method in underground coal mine based on image processing

ABSTRACT This study proposes a coal-gangue recognition method based on Retinex and extreme learning machine. The traditional Retinex has many limitations, including halo artifacts, excessive enhancement, and noise amplification in dark areas. To handle these, first, a modified multi-scale Retinex image enhancement algorithm based on hue, saturation, value color space, and bidimensional empirical mode decomposition named modified multi-scale Retinex algorithm with color restoration (MMSRCR) is proposed herein. On-site images are used to verify the effectiveness of the MMSRCR. Second, a preliminary segmentation method for coal-gangue images based on the optimal entropy threshold optimized by the improved quantum genetic algorithm is designed. The proposed segmentation method performs excellent stability compared with other traditional methods. Third, the adhesion part of the ore is further segmented based on the morphology opening operation and watershed algorithm. Then, the feature space of each enclosed area is extracted using the chain code and line segment tables. The improved extreme learning machine completes the classification of these areas, and the coal-gangue visual ratio recognition method is proposed. Finally, an experimental platform for visual ratio recognition is built to verify the proposed method. The experimental results show that the proposed method can accurately identify the visual ratio of coal-gangue.

Breast cancer classification application based on QGA-SVM

Early diagnosis of breast cancer plays an important role in improving survival rate. Physiological changes of breast tissue can be observed and measured through medical electrical impedance, and the results can be used as a preliminary diagnosis by doctors before treatment. In this paper, quantum genetic algorithm (QGA) and support vector machine (SVM) were combined to classify breast tissues to help clinicians in diagnosis. The algorithm uses QGA to optimize the parameters of SVM and improve the classification performance of SVM. In this experiment, the electrical impedance data measured from breast tissue provided by UCI [58] was used as the data set. Objectively speaking, the data volume of the data set is small and the representativeness is not strong enough. However, the experimental results show that QGA-SVM shows better classification performance, and it is better than SVM.

A Scheduling Method for Heterogeneous Signal Processing Platforms Based on Quantum Genetic Algorithm

Currently, many problems such as variable signal resources, complex execution environments, and low efficiency of scheduling algorithms are faced by heterogeneous signal processing platforms. The task scheduling algorithm is one of the key factors that directly affect the performance of the processing platform. In order to solve the problems of low efficiency of task scheduling algorithms and high computational cost of processors, a heterogeneous platform scheduling algorithm based on the quantum genetic algorithm is proposed in this paper. The algorithm constructs a task scheduling model by using a directed acyclic graph. This paper quantifies the mapping relationship between the quantum genetic algorithm and task scheduling. It corresponds qubits to binary, chromosomes to processor numbers, and individuals to processor scheduling strategies. In this paper, a new way of coding chromosomes using quantum coherence properties is designed to reduce the population size and increase population diversity. Crossover operations are performed on all individuals using full-interference crossover to avoid the results falling into local optimal solutions. The population of slow convergence is solved by implementing mutation operations on populations through quantum rotation gates. In addition, a task pre-ordering stage is designed based on the table scheduling algorithm. The task scheduling priority developed at this stage is used as the reference value for the initial encoding of the population, so that the search space for solutions is reduced. Finally, experiments are conducted using randomly generated task graphs. The algorithm is compared with improved genetic algorithms and existing intelligent scheduling algorithms. The results show that the algorithm can still obtain better results when the number of populations and iterations is small. It is more appropriate for heterogeneous platforms and computation-intensive tasks.

A Novel Strong S-Box Design Using Quantum Crossover and Chaotic Boolean Functions for Symmetric Cryptosystems

In this paper, we propose a new method for drawing a cryptographically strong substitution box using the Lorenz system and quantum genetic algorithm techniques. We used the chaotic function to generate an initial random sequence of bits and the quantum crossover to provide a new and improved substitution box with increased non-linearity. The aim of the proposed algorithm was to generate a strong and secure substitution box that can be utilized in symmetric cryptosystems. The use of chaotic Boolean functions, genetic algorithm techniques, and the quantum crossover helped achieve this goal, and statistical tests further confirmed the randomness and efficiency of the generated substitution box. The results of the statistical test suite showed that the substitution box produced by the proposed algorithm is a “pass” in terms of randomness and has strong cryptographic properties. The tests include a frequency (monobit) test, a frequency test within a block, a linear complexity test, an approximate entropy test, and a cumulative sums test among others. The p-values obtained in the tests indicate that the randomness of the generated substitution box meets the requirements of a cryptographically secure substitution box.

QGA–QGCNN: a model of quantum gate circuit neural network optimized by quantum genetic algorithm

QGA–QGCNN: a model of quantum gate circuit neural network optimized by quantum genetic algorithm

Can quantum genetic algorithm really improve quantum backpropagation neural network?

Can quantum genetic algorithm really improve quantum backpropagation neural network?

A fault diagnosis method for building electrical systems based on the combination of variational modal decomposition and new mutual dimensionless

The fault diagnosis of building electrical systems are of great significance to the safe and stable operation of modern intelligent buildings. In this paper, it has many problems, such as various fault types, inconspicuous fault characteristics, uncertainty of fault type and mode, irregularity, unstable signal, large gap between fault data classes, small gap between classes and nonlinearity, etc. A method of building electrical system fault diagnosis based on the combination of variational mode decomposition and mutual dimensionless indictor (VMD-MDI) and quantum genetic algorithm-support vector machine (QGA-SVM) is proposed. Firstly, the method decomposes the original signal through variational modal decomposition to obtain the optimal number of Intrinsic Mode Function(IMF) containing fault feature information. Secondly, extracts the mutual dimensionless indicator for each IMF. Thirdly, the optimal penalty coefficient C of the support vector machine and the parameter gamma (gamma) in the radial basis kernel function are selected by the quantum genetic algorithm. Finally, SVM optimized by the QGA is used to identify and classify the faults. By applying the proposed method to the experimental platform data of building electrical system, and compared with the traditional feature extraction method Empirical Mode Decomposition (EMD), Singular Value Decomposition(SVD), Local Mean Decomposition(LMD). And compared with traditional SVM, Genetic Algorithm optimized Support Vector Machine (GA-SVM), One-Dimensional Convolutional Neural Network (1DCNN) for fault classification methods. The experimental results show that the method has better effect and higher accuracy in fault diagnosis and classification of building electrical system. Its average test accuracy can reach 91.67%.

An ensemble learning model based on differentially private decision tree

Using differential privacy to provide privacy protection for classification algorithms has become a research hotspot in data mining. In this paper, we analyze the defects in the differentially private decision tree named Maxtree, and propose an improved model DPtree. DPtree can use the Fayyad theorem to process continuous features quickly, and can adjust privacy budget adaptively according to sample category distributions in leaf nodes. Moreover, to overcome the inevitable decline of classification ability of differentially private decision trees, we propose an ensemble learning model for DPtree, namely En-DPtree. In the voting process of En-DPtree, we propose a multi-population quantum genetic algorithm, and introduce immigration operators and elite groups to search the optimal weights for base classifiers. Experiments show that the performance of DPtree is better than Maxtree, and En-DPtree is always superior to other competitive algorithms.

Hybrid small-signal model parameter extraction for GaN HEMT based on QGA

ABSTRACT A novel algorithm is proposed for optimal extraction of GaN HEMT small-signal model parameters. The proposed Quantum Genetic Algorithm (QGA) exploits the superposition, entanglement and interference of quantum states, which solves the problems of high number of iterations and slow convergence when obtaining optimal solutions using Genetic Algorithms (GA). Meanwhile, it is solved that the Particle Swarm Optimisation (PSO) algorithm produces premature convergence and easily falls into the local optimum solution. In order to avoid the influence of distributed parasitic effects in large size devices under high-frequency conditions, a suitable frequency range is determined and combined with direct extraction techniques to determine the range of parameter values. The model parameter values are optimised step by step using QGA. In order to verify the superiority of QGA, QGA and PSO algorithms are both used to optimise GaN HEMT small-signal model parameters. By comparing the modelled S-parameter effects of the QGA and the PSO algorithm, it can be found that the QGA has better consistency with the measured data.

Selective quantum ensemble learning inspired by improved AdaBoost based on local sample information

In ensemble learning, random subspace technology not only easily loses some important features but also easily produces some redundant subspaces, inevitably leading to the decline of ensemble learning performance. In order to overcome the shortcomings, we propose a new selective quantum ensemble learning model inspired by improved AdaBoost based on local sample information (SELA). Firstly, SELA combines information entropy and random subspace to ensure that the important features of the classification task in each subspace are preserved. Then, we select the base classifier that can balance accuracy and diversity among a group of base classifiers generated based on local AdaBoost in each iteration. Finally, we utilize the quantum genetic algorithm to search optimal weights for base learners in the label prediction process. We use UCI datasets to analyze the impact of important parameters in SELA on classification performance and verify that SELA is usually superior to other competitive algorithms.

Investigation of electronic structure of a lithium atom confined by a finite spherical cavity

In this study, we have calculated the average energies of the ground and excited configurations of a lithium atom enclosed by a spherical potential well with finite barrier height, and we have also carried out calculations of orbital energies, confining potential energies and probabilities of finding the electrons in 1s and nl orbitals as a function of confinement radius for the 1s2nl configurations [nl = 2s, 2p, 3d and 4f]. Quantum Genetic Algorithm (QGA) and Hartree-Fock Roothaan (HFR) methods are used to calculate the energy eigenvalues and corresponding wave functions of the confined system. The results show that potential barrier height and confinement radius have a strong influence on the energies and the probabilities of finding the electrons inside and outside the well. It is found that 1s orbital energy increases suddenly at a critical confinement radius, at which the electron in nl orbital is trapped by the confining potential.

Optimization of Impedance-Accelerated Inverse-Time Over-Current Protection Based on Improved Quantum Genetic Algorithm

This paper proposes an impedance-accelerated inverse-time over-current protection optimization scheme based on the improved quantum genetic algorithm. First, the speed of remote backup protection is improved by increasing the optimization level of backup protection. Second, to ensure the coordination of protection when the distributed generation is connected to the distribution network, a mathematical model for the optimization of inverse time protection parameters is established. The mathematical model takes the minimum total action time of the optimized main and backup protection as the objective function, and the selectivity and sensitivity requirements of the protection as the constraints. In addition, the genetic algorithm is improved from four aspects: coding method, population initialization, quantum revolving gate, and variational evolution. The theoretical analysis and simulation results show that the proposed scheme can effectively improve the selectivity and operation speed of the protection.

Health State Identification Method of Nuclear Power Main Circulating Pump Based on EEMD and OQGA-SVM

Main circulation pump is the only high-speed rotating equipment in primary loop of nuclear power plant. Its function is to ensure the normal operation of primary loop system by controlling the circulating flow of reactor coolant. In order to ensure long-term healthy operation of nuclear power main circulating pump, a method for identifying the health states of nuclear power main circulating pump based on ensemble empirical mode decomposition (EEMD) and support vector machine optimized by optimized quantum genetic algorithm (OQGA-SVM) is proposed. Vibration signal of main circulating pump is decomposed by EEMD. Vibration signal characteristics of nuclear power main circulating pump in healthy state and different fault states are analyzed and target characteristic indexes are put forward. Then, health state identification model of main circulation pump of OQGA-SVM is established, and target characteristic indexes are used as input parameter of the model. Finally, combined with experimental data, the model analysis and validation show that the health state identification method of nuclear power main circulating pump based on EEMD-OQGA-SVM can accurately and effectively identify the states of main circulation pump, has a higher identification accuracy than EEMD-SVM method and is more efficient and accurate than EEMD-QGA-SVM method.