Quantum Genetic Algorithm Research Articles

Application of an Improved State Feedback Control in the Selective Catalytic Reduction Denitrification Systems of Coal Power Units Under Variable Load Conditions

Selective catalytic reduction (SCR) flue gas denitrification systems are inherently complex, typically embodying characteristics of non-linearity, significant time delays, and susceptibility to multiple disturbances. In the context of coal power units engaging in deep load cycling and rapid frequency adjustment, conventional proportional-integral-derivative (PID) control struggles to meet the demands of effective control. This study introduces a control strategy that incorporates a “state observer + Linear Quadratic Regulator (LQR) state feedback + Improved Quantum Genetic Algorithm (IQGA) optimized PID”. Initially, local linear mathematical models of an SCR denitrification system at 340 MW, 450 MW, and 540 MW loads were used to design state observer and LQR state feedback control parameters for each operational condition. At a single load point, the IQGA was employed to optimize the outer loop PID parameters, followed by simulation experiments of load increases and decreases between 340 MW and 540 MW. The results demonstrated that, compared to two other strategies, the proposed approach reduced the overshoot by a minimum of 1.5% and shortened the adjustment time by 31.7% under conditions of step disturbances and internal perturbations. Throughout variable operational conditions, the strategy consistently exhibited minimal output fluctuations, rapid adjustment capabilities, strong disturbance rejection, and robust stability. This algorithm proves to be an effective method for controlling NOx concentrations, offering insights for precise ammonia injection control in future applications.

Investigation of the Electronic Structure in GaAs/AlxGa1‐xAs Quantum Dots with Four Electrons

AbstractIn this paper, a detailed analysis of the electronic structure of four‐electron quantum dots is performed with finite confinement potential by a modified variational optimization approach based mainly on the quantum genetic algorithm and the Hartree‐Fock‐Roothaan method. For the ground and higher excited configurations, our analysis covers a range of parameters like the average energies of ground and excited states, singlet and triplet state energies, orbital energies, and two‐electron Coulomb and exchange interaction energies. One‐electron kinetic energy, the Coulomb potential energy between electrons and impurity, the confinement potential energy for the electrons, and the probability of finding an electron inside or outside the quantum well are also studied. The results demonstrate that both spatial confinement and the height of the potential barrier have a pronounced effect on all energies in the strong and intermediate confinement regions, but this influence weakens significantly in large dot radii. The most substantial difference between singlet and triplet energies occurs in the 1s22s2p configurations, with this difference decreasing in higher configurations. Significant increases in the 1s and 2s orbital energies are observed at the dot radii where the 2p, 3d, and 4f electrons from the outermost orbit begin to penetrate the well.

Mploying Virial Coefficients for Optimal Solutions in Variational Calculations

In this study, virial coefficients for one and two-electron hydrogen and helium-like quantum dot structures confined in an infinite potential well were calculated. The virial coefficients were determined based on the dot radius using the Quantum Genetic Algorithm (QGA) method. Calculations were performed using Fernandez's expression; however, due to calculation errors in confined systems, this equivalent expression was found unsuitable as a direct stopping criterion. Instead, virial coefficients were calculated using the ⟨T⟩/⟨V⟩ relationship, and the results were plotted. The fitting function obtained for the virial coefficients is proposed as an effective cutoff criterion for electronic structure calculations of quantum dot systems.

Comparison of Performance of Amazon Braket Using a Quantum Genetic Algorithm

Comparison of Performance of Amazon Braket Using a Quantum Genetic Algorithm

A displacement prediction model Based on IQGA-LSSVM for Mogu toppling rock slope in Lianghekou reservoir, China

Predicting landslide displacement is essential for developing early warning systems, assessing disaster risks, and understanding spatiotemporal evolution patterns. To address the limitations of insufficient accuracy in traditional landslide displacement prediction models, this study proposes a landslide displacement prediction model that combines an improved quantum genetic algorithm (IQGA) with a least squares support vector machine (LSSVM). The Mogu toppling rock slope is employed as a case study to demonstrate the application of the empirical mode decomposition (EMD) method for the decomposition of landslide displacement into trend and periodic components. Subsequently, these components are accurately predicted using Fourier series fitting and the IQGA-LSSVM model, respectively. The prediction results are subsequently evaluated and analyzed. The findings indicate that this model outperforms traditional QGA-LSSVM models, BP neural networks, and Support Vector Machine models in terms of prediction accuracy. Furthermore, the study illustrates how high-accuracy landslide displacement prediction models can effectively prevent and control disasters in critical areas such as hydraulic and hydropower engineering, urban planning, and geological disaster management.

Optimized memory allocation in edge-PLCs using Deep Q-Networks and bidirectional LSTM with Quantum Genetic Algorithm

This paper offers a fresh perspective to memory allocation in edge-PLCs within industrial IoT environments, leveraging a hybrid a structure that incorporates Deep Q-Networks (DQN) and Bidirectional Long Short-Term Memory Networks (BiLSTM), complemented by Quantum Genetic Algorithm Optimization. In addressing the dynamic challenges of memory management, our method combines the reinforcement learning capabilities of DQN with the temporal contextualization provided by BiLSTM networks. The DQN component learns to optimize memory allocation policies based on immediate rewards and feedback, while the BiLSTM network captures long-term dependencies in data, enhancing predictive modeling for future data arrival rates. Moreover, we introduce Quantum Genetic Algorithm Optimization, a cutting-edge approach that infuses quantum-inspired principles into the traditional genetic algorithm framework, to further refine the memory allocation process. By leveraging principles of quantum computing, this optimization algorithm explores the solution space more efficiently, allowing for faster convergence and improved performance. Through simulation experiments, we exhibit the potency of our hybrid approach in reducing data loss probability and enhancing system performance in edge-PLCs. Our findings underscore the significance of integrating advanced machine learning techniques with quantum-inspired optimization algorithms to address complex challenges in industrial IoT environments, offering a promising avenue for enhancing memory allocation efficiency in edge computing systems.

A research on resistance spot welding quality judgment of stainless steel sheets based on revised quantum genetic algorithm and hidden markov model

In view of the following problems existed in the data-driven resistance spot welding (RSW) quality judgment methods: relying on expert experience, resulting in a high misjudgment rate; affected by the initial value, it is easy to fall into the local extreme point; multiple parameters needed to be modulated to increase the training time of the model. Therefore, this paper proposed a RSW quality judgment method based on revised quantum genetic algorithm (RQGA) and hidden markov model (HMM). Which used quantum rotation gate to replace the evolution process of genetic algorithm (GA), reducing the number of parameters that needed to be modulated in the model, and optimizing the initial model of HMM by RQGA to avoid falling into local extreme point. At the same time, combining expert experience with HMM to make quality judgment to ensure the accuracy of judgment.Firstly, by RSW test to obtain the main influencing factors of quality, and combining with expert experience to realize the preliminary judgment of it, on this basis, HMM was established. Next, to address the susceptibility of HMM to initial model, quantum genetic algorithm (QGA) was utilized to search for the optimal initial model. Moreover, in view of the limitations of QGA (“coding length disaster” and fixed rotation angle), a corresponding improvement was also presented. At last, the established model was applied to the RSW process of stainless steel sheets railway carriage roof. Compared with the other five models, it took the least time to complete parameter training of HMM in four quality states, less than 15s; when judging the quality, its log-likelihood probability value was −47.8418, which was close to the maximum value of this state; during the classification test, the average classification accuracy of the four quality states were 90.8 %, 92.2 %, 90.8 % and 92.0 %, respectively, which was higher than the other five models. Consequently, the model established in this paper was effective.

Optical Properties of Three‐Electron GaAs/AlxGa1−xAs QDs with Finite Confinement Potential

AbstractIn the case of finite confinement potential, the average energies and corresponding wave functions for the 1s2nl configurations, in which nl = 2s, 2p, 3d, and 4f, of three‐electron GaAs/AlxGa1−xAs quantum dot with and without impurity are computed by using a new variational approach which is a combination of Quantum Genetic Algorithm procedure and Hartree–Fock–Roothaan method. Using the calculated average energies and wave functions, a detailed investigation of the linear, third‐order nonlinear and total absorption coefficients (ACs) and the refractive index changes (RICs) for the quantum dot is performed, and the obtained results are presented as a function of dot radius and photon energies. The results show that the dot radius, the impurity charge, and the height of potential barrier have a strong influence on the average energies and absorption spectra of the system. As the potential barrier height increases, the peak positions of the ACs and RICs shift toward higher energy, and there is a significant increase in the amplitudes of the absorption spectra as the potential barrier height increases. In addition, the electron wave functions begin to enter the quantum well at smaller dot radii with increasing barrier height.

Hybrid quantum search with genetic algorithm optimization

Quantum genetic algorithms (QGA) integrate genetic programming and quantum computing to address search and optimization problems. The standard strategy of the hybrid QGA approach is to add quantum resources to classical genetic algorithms (GA), thus improving their efficacy (i.e., quantum optimization of a classical algorithm). However, the extent of such improvements is still unclear. Conversely, Reduced Quantum Genetic Algorithm (RQGA) is a fully quantum algorithm that reduces the GA search for the best fitness in a population of potential solutions to running Grover’s algorithm. Unfortunately, RQGA finds the best fitness value and its corresponding chromosome (i.e., the solution or one of the solutions of the problem) in exponential runtime, O(2n/2), where n is the number of qubits in the individuals’ quantum register. This article introduces a novel QGA optimization strategy, namely a classical optimization of a fully quantum algorithm, to address the RQGA complexity problem. Accordingly, we control the complexity of the RQGA algorithm by selecting a limited number of qubits in the individuals’ register and fixing the remaining ones as classical values of ‘0’ and ‘1’ with a genetic algorithm. We also improve the performance of RQGA by discarding unfit solutions and bounding the search only in the area of valid individuals. As a result, our Hybrid Quantum Algorithm with Genetic Optimization (HQAGO) solves search problems in O(2(n−k)/2) oracle queries, where k is the number of fixed classical bits in the individuals’ register.

Multi-Adaptive Strategies-Based Higher-Order Quantum Genetic Algorithm for Agile Remote Sensing Satellite Scheduling Problem.

The agile remote sensing satellite scheduling problem (ARSSSP) for large-scale tasks needs to simultaneously address the difficulties of complex constraints and a huge solution space. Taking inspiration from the quantum genetic algorithm (QGA), a multi-adaptive strategies-based higher-order quantum genetic algorithm (MAS-HOQGA) is proposed for solving the agile remote sensing satellites scheduling problem in this paper. In order to adapt to the requirements of engineering applications, this study combines the total task number and the total task priority as the optimization goal of the scheduling scheme. Firstly, we comprehensively considered the time-dependent characteristics of agile remote sensing satellites, attitude maneuverability, energy balance, and data storage constraints and established a satellite scheduling model that integrates multiple constraints. Then, quantum register operators, adaptive evolution operations, and adaptive mutation transfer operations were introduced to ensure global optimization while reducing time consumption. Finally, this paper demonstrated, through computational experiments, that the MAS-HOQGA exhibits high computational efficiency and excellent global optimization ability in the scheduling process of agile remote sensing satellites for large-scale tasks, while effectively avoiding the problem that the traditional QGA has, namely low solution efficiency and the tendency to easily fall into local optima. This method can be considered for application to the engineering practice of agile remote sensing satellite scheduling for large-scale tasks.

A data-driven computational optimization framework for designing thin-walled lenticular deployable composite boom with optimal load-bearing and folding capabilities

The thin-walled lenticular deployable composite boom (LDCB) is promising for aerospace engineering applications due to its lightweight and compact nature, but its mechanical behaviors are the main challenges limiting its practical application. Here, the axial compression and folding behaviors of LDCB were numerically simulated and the simulation results were verified according to the experimental data in the literature. Then, a new double chains quantum genetic algorithm (DCQGA)-gaussian process regression (GPR) model and data-driven computational optimization framework were proposed and the new model was trained using a database with 400 simulation results, the superiority of which was demonstrated by prediction accuracy evaluation. Additionally, benchmarking five state-of-the-art algorithms found that the coupling technology of DCQGA-GPR-non-dominated sorting genetic algorithm III (NSGA-III) is an excellent optimization strategy to obtain a well-designed structure of LDCB that maximizes the effectiveness of the material while satisfying the lightweight. The optimized LDCB design exhibits a significant performance improvement, with a 26.3 % reduction in the folding moment, a 34.2 % reduction in the maximum Tsai-Hill failure index, a 43.6 % increase in the critical load, and an 11 % reduction in linear density of mass compared to the initial design.

Quantum genetic algorithm assisted high speed and precision pump-probe thermoreflectance characterization of micro-/nano-structures

The rapid and precise extraction of thermophysical properties remains an enormous challenge in pump-probe thermoreflectance, and quantum algorithms present significant potential for addressing thermal transport problems. This paper introduces an innovative application of the quantum genetic algorithm to the fitting of experimental data from pump-probe thermoreflectance. Initially, a physical model based on the thermal transmission matrix is developed to compute the heat transport of multilayer micro-/nano-structures. The quantum genetic algorithm is then employed for multiparameter optimization in the extraction of thermophysical properties and tested on three samples. Both simulation and experimental results demonstrate that the quantum genetic algorithm assisted pump-probe thermoreflectance, capable of extracting thermophysical properties with high precision in just one minute, is approximately 30 times faster than the conventional method and 10 times faster than the genetic algorithm. Furthermore, measurement sensitivity and algorithmic randomness are analyzed. The reasons behind the phenomenon of equivalent fitting results and the indication of non-convex optimization are also presented.

Quantum Genetic Algorithm With Individuals in Multiple Registers

Quantum Genetic Algorithm With Individuals in Multiple Registers

Stabilization analysis for nonlinear interconnected system with memory based coupled sampled data control via quantum-inspired genetic algorithm

In the realm of modern interconnected systems, achieving stability represents a pivotal challenge due to the complex dynamics among various components. This paper presents a new approach to stability analysis, leveraging linear matrix inequalities and Lyapunov-Krasovskii functionals. The framework of memory-based coupling sampled data control is enhanced by a quantum genetic algorithm. By incorporating linear matrix inequality approach, this study establishes a robust mathematical foundation for characterizing stability conditions and synthesizing control gains. This innovative integration provides a powerful toolset to optimize control parameters while mitigating the impact of disturbances. The simulation findings are explicitly based on experimental values of two-area interconnected power systems with doubly fed induction generator based wind farm, which guarantees the asymptotic stability of the proposed controller.

Method of sequential intention inference for a space target based on meta-fuzzy decision tree

For an operational satellite in orbit, potential risks may arise when an unknown target approaches. Therefore, it is essential for the satellite to infer the intentions of the target in space. To achieve this objective, this paper proposes a method of sequential intention inference based on meta-fuzzy decision tree. The proposed method initially defines three situational functions: flyaround, collision, and observation. These functions help organize the inherent characteristics found in the trace and relative attitude of the target. Subsequently, by combining fuzzy decision tree (FDT), spatio-temporal modeling based on Long Short-Term Memory (LSTM), and task migration using meta-learning techniques, we can realize sequential intention inference for the target while ensuring quick adaptation to new tasks. Additionally, our proposed method takes into account the impact of fuzzy elements within FDT. Through quantum genetic algorithm (QGA) optimization, these fuzzy elements can be fine-tuned to enhance intention inference accuracy.

Research on Application of Improved Quantum Optimization Algorithm in Path Planning

For the building emergency evacuation path planning problem, existing algorithms suffer from low convergence efficiency and the problem of getting trapped in local optima. The Bloch Spherical Quantum Genetic Algorithm (BQGA) based on the least-squares principle for single-robot path planning and Bloch Spherical Quantum Bee Colony Algorithm (QABC) for multi-robots path planning are studied. Firstly, the characteristics of three-dimensional path planning are analyzed, and a linear decreasing inertia weighting approach is used to balance the global search ability of chromosomes and accelerate the search performance of the algorithm. Then, the application algorithm can generate a clear motion trajectory in the raster map. Thirdly, the least squares approach is used to fit the results, thus obtaining a progressive path. Finally, multi-robots path planning approaches based on QABC are discussed, respectively. The experimental results show that BQGA and QABC do not need to have a priori knowledge of the map, and they have strong reliability and practicality and can effectively avoid local optimum. In terms of convergence speed, BQGA improved by 3.39% and 2.41%, respectively, while QABC improved by 13.31% and 17.87%, respectively. They are more effective in sparse paths.

Design of low-correlation sidelobe FSK-PSK waveforms with controllable frequency points

Frequency shift keying-phase shift keying (FSK-PSK) waveforms are recognized for their superior resolution and signal detectability. However, the optimization of FSK-PSK parameters, particularly frequency hopping control, remains challenging and can lead to increased sidelobe levels. This paper develops an optimization model to substantially enhance the controllability of hopping frequency in FSK-PSK waveforms for radar systems. By harnessing the Karush-Kuhn-Tucker (KKT) conditions, the model strategically minimizes the autocorrelation integrated sidelobe level (ISL), thereby sharpening radar target discrimination and bolstering anti-jamming capabilities. An iterative algorithm specifically addresses the model’s optimal subproblems, optimizing frequency agility and stability. Simulation results show that our FSK-PSK waveform outperforms the waveform optimized by the Multi-Objective Quantum Genetic Algorithm (MoQGA) with a 4.93 dB sidelobe suppression gain, enhancing detection accuracy and anti-jamming performance in disturbed environments.

Optimization of Charging or Discharging Strategy of Energy Storage in Multi-Objective Market Transactions Based on Quantum Genetic Algorithm

Multi-objective energy optimization is critical for ensuring stable and power system operation securely. Though, multi-objective energy optimization is difficult because of an interdependence and opposing goals. To address conflicting objectives, a multi-objective optimization model is required, hence Monarch Butterfly African Vulture Optimization Algorithm (MBAVOA) is newly proposed for resolving multi objective issues. MBAVOA is the hybridized of two optimization algorithms, which includes Monarch Butterfly Optimization (MBO), and African Vulture Optimization Algorithm (AVOA). Here, charging cost, distance, and the user convenience are optimized while taking Renewable Energy Sources (RES) into consideration using MBAVOA. In addition, a load is computed using a Quantum Genetic Algorithm (QGA), describes intermittent and uncertain RES, such as wind and solar. The QGA-MBAVOA outperformed with the least charging cost 63%, fitness 0.010, and user convenience 0.819.

Designing asynchronous resilient sampled data controller for nonlinear Markov jump systems via hidden Markov model and quantum genetic algorithm

The article delves into the problem of stochastic stability analysis for nonlinear Markov jump systems (MJSs) with fuzzy asynchronous resilient sampled-data controllers (SDCs) using hidden Markov model (HMM) and quantum genetic algorithms (QGAs). A novel framework using QGA has been developed to obtain the control gain matrices by solving the linear matrix inequality (LMI). A new criterion is proposed to guarantee the stochastic stability of fuzzy nonlinear MJSs, relying on the mode-dependent Lyapunov-Krasovskii functional (LKF) approach. Standard Chua’s circuit has been used to show the reliability and value of the developed results. Also, the Lorentz system has been explored to demonstrate the non-stochastic case.

Line Loss Calculation and Optimization in Low Voltage Lines with Photovoltaic Systems Using an Analytical Model and Quantum Genetic Algorithm

Line Loss Calculation and Optimization in Low Voltage Lines with Photovoltaic Systems Using an Analytical Model and Quantum Genetic Algorithm