Quantum Genetic Algorithm Research Articles

Magnetic Anomaly Detection Based on a Compound Tri-Stable Stochastic Resonance System.

In the case of strong background noise, a tri-stable stochastic resonance model has higher noise utilization than a bi-stable stochastic resonance (BSR) model for weak signal detection. However, the problem of severe system parameter coupling in a conventional tri-stable stochastic resonance model leads to difficulty in potential function regulation. In this paper, a new compound tri-stable stochastic resonance (CTSR) model is proposed to address this problem by combining a Gaussian Potential model and the mixed bi-stable model. The weak magnetic anomaly signal detection system consists of the CTSR system and judgment system based on statistical analysis. The system parameters are adjusted by using a quantum genetic algorithm (QGA) to optimize the output signal-to-noise ratio (SNR). The experimental results show that the CTSR system performs better than the traditional tri-stable stochastic resonance (TTSR) system and BSR system. When the input SNR is -8 dB, the detection probability of the CTSR system approaches 80%. Moreover, this detection system not only detects the magnetic anomaly signal but also retains information on the relative motion (heading) of the ferromagnetic target and the magnetic detection device.

Design and Implementation of Vehicle Scheduling Optimization for Smart Logistics Platform Powered by Hadoop Big Data

E-commerce has become the mainstream consumption mode for people, and with the continuous increase of online shopping business volume, controlling logistics costs has become an urgent problem to be solved. In order to solve the challenges in vehicle scheduling and achieve a scientific and reasonable vehicle scheduling scheme, this research is based on Hadoop Big data platform, introduces the concept of time axis, and builds a vehicle scheduling model based on Hadoop intelligent platform. Based on quantum genetic algorithm and combined with MapReduce model, a quantum improved genetic algorithm is constructed to solve vehicle scheduling optimization problems. The results show that the traditional quantum genetic algorithm converges after 20 iterations, achieving an optimal value of 575 and taking 360 seconds. The improved quantum genetic algorithm converged after 10 iterations and achieved an optimal value of 675, taking 200 seconds. Compared with quantum genetic algorithm, the improved quantum genetic algorithm reduces the time spent by 44.4%. Selecting customer data from a certain logistics company for testing, the improved algorithm shortens the delivery time and achieves the design of the optimal path scheduling plan. This study optimized transportation routes and resource scheduling, reduced transportation costs, and played an important role in optimizing vehicle scheduling in the logistics industry.

An optimization approach for environmental control using quantum genetic algorithm and support vector regression

Photosynthesis serves as the foundation for vegetable crop yield. It is crucial to appropriately regulate the environmental parameters associated with photosynthesis to ensure efficient production and energy conservation in plant factories or greenhouses. In this research, we proposed a novel optimization approach for determining the target value of environmental control, aiming to balance plant growth and energy cost. By employing hydroponic lettuces as experimental samples, we measured their photosynthetic rates (Pn) under various combinations of four environmental factors: air temperature (AT), nutrient solution temperature (NST), photon flux density (PFD), and CO2 concentration ([CO2]). The photosynthetic data were combined with the support vector regression algorithm to develop a Pn prediction model. This model achieved a coefficient of determination of 0.9748, a root mean square error value of 0.9302 µmol∙m−2∙s−1, and a mean absolute error value of 1.1813 µmol∙m−2∙s−1. The model provide data for subsequent environmental control. The quantum genetic algorithm (QGA) was employed to search the optimal Pn and corresponding PFD, [CO2], and NST at different ATs. The fitness function for QGA was developed considering both the Pn and the energy consumption. This approach could calculate the target environments (PFD, [CO2], and NST) for any given AT. Compared with the Pn maximization approach, the energy cost-saving rate was 1.5 to 3.5 times higher than the Pn loss. The proposed approach could quickly and accurately determine an optimal environmental control target value, outperforming other approaches in complexity and generality. Thus, this study offers an elegant approach to environmental control for hydroponic cultivation.

Energy efficiency optimisation in massive multiple‐input, multiple‐output network for 5G applications using new quantum genetic algorithm

AbstractDevising efficient optimisation methods has been a subject of great research attention since current evolving trends in communication networks, machine learning, and other cutting‐edge systems that need a fast and accurate optimised computational model. Classical computers became incapable of handling new optimisation problems posed by newly emerging trends. Quantum optimisation algorithms appear as alternative solutions. The existing bottleneck that restricts the use of the newly developed quantum strategies is the limited qubit size of the available quantum computers (the size of the most recent universal quantum computer is 433 qubits). A new quantum genetic algorithm (QGA) is proposed that handles the presented problem. A quantum extreme value searching algorithm and quantum blind computing framework are utilised to extend the search capabilities of the GA. The quantum genetic strategy is exploited to maximise energy efficiency at full spectral efficiency of massive multiple‐input, multiple‐output (M‐MIMO) technology as a toy example for pointing out the efficiency of the presented quantum strategy. The authors run extensive simulations and prove how the presented quantum method outperforms the existing classical genetic algorithm.

The improved quantum genetic algorithm with its application in fault diagnosis

In this paper, by analyzing the characteristics of the simulated annealing algorithm (SA) and the real double-chain coding target gradient quantum genetic algorithm (DCQGA), the real double-chain coding target gradient quantum genetic simulated annealing algorithm (DCQGSAA) is proposed. Because the performance of LSSVR is extraordinarily sensitive to its key parameters, the proposed algorithm is used to optimize these parameters, then a hybrid non-parametric prediction model is put forward. This model is used in fault prediction of liquid rocket engine thrust. The simulation results show that the proposed model is effective for small samples and multi-dimensional fault prediction.

A novel quantum genetic algorithm with the application in LS-SVR

The characteristics of LS-SVR are analyzed. LS-SVR is fitted for modeling small samples and high dimensional data, but the performance of LS-SVR is related to the specific data distribution, the kind of kernel function, its related kernel parameter, and the penalty coefficient. In this paper, the radial basis function is applied as the kernel function of LS-SVR, and the real double-chain coding target gradient quantum genetic algorithm (DCQGA) is applied to optimize the kernel parameter and penalty item coefficient of LS-SVR, then the regression prediction model DCQGALSSVR is proposed. It is of great significance to build an accurate and reliable fault prediction model for the health monitoring and fault diagnosis of liquid rocket engines. The thrust of a liquid rocket engine is an important factor in its health monitoring. By predicting the thrust change value and comparing the predicted value with the engine thrust threshold, it can be predicted whether the engine will fail at a certain time. In this paper, the proposed DCQGALSSVR model is used to model the thrust of a liquid rocket engine. The simulation results show that the average relative error is 0.37% using LSSVR for modeling on 12 test samples, and is 0.3186% using DCQGALSSVR on the same samples. It can be seen that DCQGALSSVR is effective for the health monitoring of liquid rocket engines, so it has a certain promotion significance.

Gain optimization and data modeling of S-band of Tm-doped fiber amplifier based on genetic algorithm

At present, fiber amplifiers are indispensable in the field of communication, Erbium-doped fiber amplifiers (EDFA) are currently more mature fiber amplifiers, but because of electronic bottlenecks, they have approached saturation in recent years. Thus, the study and application of fiber amplifiers doped with other rare earth elements has become a new goal at present. At the same time, expanding the available bandwidth of the fiber amplifier to a higher or lower direction has also become a major task. This article discusses the gain of thulium-doped fiber amplifiers at 1400 nm – 1450 nm (S-band) and uses the genetic algorithm to optimize the fiber length and doping concentration to find the optimal solution, this article also analyzes the change in gain at different fiber lengths and doping concentrations. The results show that the fiber length is 56.2705 meters, and the doping concentration is 59.9502 ×1024 per square meter, at which the gain reaches the peak is 65.19. The results show that the thulium-doped fiber amplifier (TDFA) in this band has good amplification characteristics, and it can be meticulously studied based on this method in the future to obtain more accurate results. It can also be solved using other algorithms, such as Quantum Genetic Algorithms.

An Enhanced Quantum Genetic Algorithm and Its Application in the Health Monitoring of a Rocket Engine

An Enhanced Quantum Genetic Algorithm and Its Application in the Health Monitoring of a Rocket Engine

Research on support vector machine optimization based on improved quantum genetic algorithm

Research on support vector machine optimization based on improved quantum genetic algorithm

New Quantum Genetic Algorithm Based on Constrained Quantum Optimization

New Quantum Genetic Algorithm Based on Constrained Quantum Optimization

Extraction and diagnosis of rolling bearing fault signals based on improved wavelet transform

As the continuous growth of the machinery industry, the importance of rolling bearings as key connecting parts in machinery movement is also increasing. However, the extraction and diagnosis of rolling bearing fault signals are difficult, and how to use modern transform analysis methods to raise the extraction efficiency and diagnostic accuracy becomes the focus. For this, a rolling bearing fault signal extraction and diagnosis model is designed based on empirical wavelet transform. The diagnostic model is optimized by using support vector machine and quantum genetic algorithm to design a rolling bearing fault signal extraction and diagnosis model based on improved empirical wavelet transform-support vector machine. The test results show that the research method can obtain four component signals showing different anomalies when generating time domain diagrams. Only five component peaks are generated and one group is extracted as output when generating component peaks. The abnormal amplitude of envelope spectrum basically reaches 0.40×10-6 or above. The judgment accuracy of component diagnosis reaches 98.12%. The above results show that the research method has better fault signal extraction ability and better diagnostic accuracy when performing fault signal diagnosis, which can provide new technical support for rolling bearing fault signal extraction and diagnosis.

3D lidar SLAM-based systems in object detection and navigation applications

ABSTRACT This paper considered an object detection system based on 3D LiDAR Sensor and Simultaneous Localization and Mapping (SLAM) to complete the navigation applications of mobile robots. A 3D-based SLAM with lightweight and ground-optimized Lidar odometry and mapping (LeGO-LOAM) appropriately generated the environmental maps. SLAM is a tool used to obtain information from the environment, allowing mobile robots to know their location. Indoor environment data is immedicably created while SLAM is processing the information. The dynamic object detection algorithm depends on the available information to realize the external morphology and circle the bounding box of moving objects. Therefore, a wheeled mobile robot (WMR) was employed to dynamically trace the object’s movement direction. Finally, This study found that the quantum genetic algorithm (QGA) is more efficient in generating a shorter path than the particle swarm optimization, and a dynamic window approach (DWA) is immediately detected as a dynamic obstacle. Therefore, WMR obtains enough object, obstacle, and routing information to effectively and safely reach the destination through the Move_base software package in Robot Operating System.

Dynamic range optimization of optoelectronic systems based on quantum mathematical models

A photodiode is a small and low-cost photosensitive sensor that can measure the solar vector through at least three combinations, making it a high-precision solar sensor. How to select the number of photodiodes and determine their layout is a challenge in order to solve the solar vector in any direction in a 360° field of view space as much as possible, the author first discretizes the equal surface area of the 360° field of view space, transforming the infinite sensor layout optimization problem into a finite combinatorial optimization (NP problem is difficult). Then, by establishing multi-objective optimization functions for coverage and uniformity, and combining quantum genetic algorithm to solve the optimal solution. The experimental analysis of the layout effects of different numbers of photodiodes and field of view ranges provides a theoretical basis for optimizing the layout of multiple photodiodes. The experimental results show that selecting 12 to 14 photodiodes can basically achieve a layout without coverage risk and uniform risk.

Research on Mental Workload of Deep-Sea Oceanauts Driving Operation Tasks from EEG Data.

A person's present mental state is closely associated with the frequency and temporal domain features of spontaneous electroencephalogram (EEG) impulses, which directly reflect neurophysiological signals of brain activity. EEG signals are employed in this study to measure the mental workload of drivers while they are operating a vehicle. A technique based on the quantum genetic algorithm (QGA) is suggested for improving the kernel function parameters of the multi-class support vector machine (MSVM). The performance of the algorithm based on the quantum genetic algorithm is found to be superior to that of other ways when other methods and the quantum genetic algorithm are evaluated for the parameter optimization of kernel function via simulation. A multi-classification support vector machine based on the quantum genetic algorithm (QGA-MSVM) is applied to identify the mental workload of oceanauts through the collection and feature extraction of EEG signals during driving simulation operation experiments in a sea basin area, a seamount area, and a hydrothermal area. Even with a limited data set, QGA-MSVM is able to accurately identify the cognitive burden experienced by ocean sailors, with an overall accuracy of 91.8%.

A Quantum Genetic Algorithm for Building a Semantic Textual Similarity Estimation Framework for Plagiarism Detection Applications.

The majority of the recent research on text similarity has been focused on machine learning strategies to combat the problem in the educational environment. When the originality of an idea is copied, it increases the difficulty of using a plagiarism detection system in practice, and the system fails. In cases like active-to-passive conversion, phrase structure changes, synonym substitution, and sentence reordering, the present approaches may not be adequate for plagiarism detection. In this article, semantic extraction and the quantum genetic algorithm (QGA) are integrated in a unified framework to identify idea plagiarism with the aim of enhancing the performance of existing methods in terms of detection accuracy and computational time. Semantic similarity measures, which use the WordNet database to extract semantic information, are used to capture a document's idea. In addition, the QGA is adapted to identify the interconnected, cohesive sentences that effectively convey the source document's main idea. QGAs are formulated using the quantum computing paradigm based on qubits and the superposition of states. By using the qubit chromosome as a representation rather than the more traditional binary, numeric, or symbolic representations, the QGA is able to express a linear superposition of solutions with the aim of increasing gene diversity. Due to its fast convergence and strong global search capacity, the QGA is well suited for a parallel structure. The proposed model has been assessed using a PAN 13-14 dataset, and the result indicates the model's ability to achieve significant detection improvement over some of the compared models. The recommended PD model achieves an approximately 20%, 15%, and 10% increase for TPR, PPV, and F-Score compared to GA and hierarchical GA (HGA)-based PD methods, respectively. Furthermore, the accuracy rate rises by approximately 10-15% for each increase in the number of samples in the dataset.

An Optimal Operation Strategy of Regenerative Electric Heating Considering the Difference in User Thermal Comfort

Regenerative electric heating has gradually become one of the main forms of winter heating with the promotion of “coal to electricity” project. By fully exploiting its regulating capacity, it can effectively achieve a win–win situation of “peak shaving and valley filling” on the grid side and “demand response” on the customer side. In order to meet the different heating demands of users, a regenerative electric heating optimization and control strategy is proposed, taking into account the difference in users’ thermal comfort. Firstly, the reasons for the difference in user thermal comfort are analyzed, and the differentiated preference factors are calculated based on the maximum likelihood estimation method to design differentiated heating schemes. Then, a dynamic optimization and control model for regenerative electric heating with comfort and economic evaluation indicators is established and solved by using quantum genetic algorithm. Finally, a numerical example is used for simulation analysis. The research results show that the strategy proposed in this paper can take into account the comfort of customers and the economy of peaking and low load shifting, so that the operation of regenerative electric heating can respond to the different needs of different customer groups, and realize flexible adjustment at any time of the day.

Autonomous detection of myocarditis based on the fusion of improved quantum genetic algorithm and adaptive differential evolution optimization back propagation neural network.

Myocarditis is cardiac damage caused by a viral infection. Its result often leads to a variety of arrhythmias. However, rapid and reliable identification of myocarditis has a great impact on early diagnosis, expedited treatment, and improved patient survival rates. Therefore, a novel strategy for the autonomous detection of myocarditis is suggested in this work. First, the improved quantum genetic algorithm (IQGA) is proposed to extract the optimal features of ECG beat and heart rate variability (HRV) from raw ECG signals. Second, the backpropagation neural network (BPNN) is optimized using the adaptive differential evolution (ADE) algorithm to classify various ECG signal types with high accuracy. This study examines analogies among five different ECG signal types: normal, abnormal, myocarditis, myocardial infarction (MI), and prior myocardial infarction (PMI). Additionally, the study uses binary and multiclass classification to group myocarditis with other cardiovascular disorders in order to assess how well the algorithm performs in categorization. The experimental results demonstrate that the combination of IQGA and ADE-BPNN can effectively increase the precision and accuracy of myocarditis autonomous diagnosis. In addition, HRV assesses the method's robustness, and the classification tool can detect viruses in myocarditis patients one week before symptoms worsen. The model can be utilized in intensive care units or wearable monitoring devices and has strong performance in the detection of myocarditis.

Accident parameter prediction method for lead-bismuth cooled reactor based on a multivariate LSTM network coupled with an optimization algorithm

Accurate acquisition of key parameters of lead–bismuth cooled reactors under accident conditions is a prerequisite for reactor safety analyses. In this work, four optimization algorithms(Particle Swarm Algorithm, Genetic algorithm, Quantum genetic algorithm, Whale optimization algorithm) are used to improve the forecast efficiency of a long short-term memory (LSTM) neural network using hyperparameter optimization, and a method is developed to predict the parameters of a lead–bismuth cooled reactor (MARS-3) during a loss of flow accident. A comprehensive evaluation of the proposed method is performed using the TOPSIS technique based on the data samples generated using the sub-channel code SUBCHANFLOW. The results show that the prediction performance of the multivariate LSTM neural network coupled with the particle swarm optimization method is optimal, and its computational efficiency is 438 times that of SUBCHANFLOW. Overall, the study findings can help improve the prediction efficiency for key thermal parameters of lead–bismuth cooled reactors and enhance the emergency response capability of such reactors.

Analysis of Dynamic Mesh Stiffness and Dynamic Response of Helical Gear Based on Sparse Polynomial Chaos Expansion

This paper presents an efficient method for obtaining the dynamic mesh stiffness and dynamic response of a helical gear pair. Unlike the traditional dynamic model that utilizes a time-dependent sequence, the mesh stiffness using the presented method is updated according to the gear displacement vector at each sub-step of the numerical calculation. Three-dimensional loaded tooth contact analysis (3D LTCA) is used to determine the mesh stiffness, and a surrogate model based on sparse polynomial chaos expansion (SPCE) is proposed to improve the computational efficiency, which is achieved by reducing the number of coefficients in the polynomial chaos expansion (PCE) model though a quantum genetic algorithm. During the calculation, the gear displacement vector at each sub-step is converted into the changes in center distance, misalignment angle, and mesh force, which are then introduced into the SPCE model to update the mesh stiffness for subsequent calculations. The results suggest that the SPCE model exhibits high accuracy and can significantly improve the computational efficiency of the PCE model, making it suitable for dynamic calculations. Upon updating the mesh stiffness during the dynamic calculation, the mesh stiffness declines, the dynamic transmission error (DTE) increases, and the frequency components of the responses change significantly.

Data-driven optimization of pollutant emission and operational efficiency for circulating fluidized bed unit

To achieve the economic and environmentally friendly operation of circulating fluidized bed (CFB) units, it is imperative to conduct optimization to obtain an economical mode of pollutant removal. This article focuses on the multi-objective optimization between SO2-NOx emissions and thermal efficiency for CFB units. According to the operation data and production mechanism of pollutants, models of SO2-NOx emission concentration, bed temperature, and oxygen content based on a convolutional neural network–bidirectional long short-term memory–attention mechanism (CNN-BiLSTM-Attention) were established. Then, an improved quantum genetic algorithm was used to find the optimal input variables of the SO2-NOx emission model. The proposed modeling method was evaluated, and it more accurately simulated the trends of actual operation data than other models under different operating conditions. Combining these models with economic calculations, the operating costs under typical conditions were reduced by 3.20% and 1.82% respectively, and the thermal efficiency increased by 0.72% and 1.07%, which contributes to economical and intelligent operation of the unit.