Quantum Genetic Algorithm Research Articles

Emergency shelter allocation planning technology for large-scale evacuation based on quantum genetic algorithm.

Shelter allocation is one of the most important measures in urban disaster prevention and mitigation planning. Meanwhile, it is essentially a comprehensive planning problem combining resource allocation and traffic routing. A reasonable allocation scheme can avoid congestion, improve evacuation efficiency, and reduce the casualty rate. Owing to the large region and large evacuation population demand, quickly solving the complex allocation problem is somewhat challenging, and thus, the optimal results are difficult to obtain with the increase of evacuation scale by traditional allocation methods. This article aims to establish a shelter allocation model for large-scale evacuation, which employs an improved quantum genetic algorithm (IQGA) based on spreading operation and considering the total evacuation distance, the capacity constraint of evacuation sites, and the dispersion of allocation results, and compare allocation schemes of the spreading model with those of models that consider different constraints. Results show that the allocation model with the spreading operation has better allocation results than that without the spreading operation. For the allocation model with spreading operation, the spreading model with different spreading speeds is more reasonable than that with the same spreading speed, and the allocation results are closer to the ideal results with the increase of constraints. In addition, according to the allocation results, the evacuation route map and the evacuation heat map are drawn to intuitively understand the distribution scheme of each shelter.

LQR Trajectory Tracking Control of Unmanned Wheeled Tractor Based on Improved Quantum Genetic Algorithm

In the process of trajectory tracking using the linear quadratic regulator (LQR) for driverless wheeled tractors, a weighting matrix optimization method based on an improved quantum genetic algorithm (IQGA) is proposed to solve the problem of weight selection. Firstly, the kinematic model of the wheeled tractor is established according to the Ackermann steering model, and the established model is linearized and discretized. Then, the quantum gate rotation angle adaptive strategy is optimized to adjust the rotation angle required for individual evolution to ensure a timely jumping out of the local optimum. Secondly, the populations were perturbed by the chaotic perturbation strategy and Hadamard gate variation according to their dispersion degree in order to increase their diversity and search accuracy, respectively. Thirdly, the state weighting matrix Q and the control weighting matrix R in LQR were optimized using IQGA to obtain control increments for the trajectory tracking control of the driverless wheeled tractor with circular and double-shifted orbits. Finally, the tracking simulation of circular and double-shifted orbits based on the combination of Carsim and Matlab was carried out to compare the performance of LQR optimized by five algorithms, including traditional LQR, genetic algorithm (GA), particle swarm algorithm (PSO), quantum genetic algorithm (QGA), and IQGA. The simulation results show that the proposed IQGA speeds up the algorithm’s convergence, increases the population’s diversity, improves the global search ability, preserves the excellent information of the population, and has substantial advantages over other algorithms in terms of performance. When the tractor tracked the circular trajectory at 5 m/s, the root mean square error (RMSE) of four parameters, including speed, lateral displacement, longitudinal displacement, and heading angle, was reduced by about 30%, 1%, 55%, and 3%, respectively. When the tractor tracked the double-shifted trajectory at 5 m/s, the RMSE of the four parameters, such as speed, lateral displacement error, longitudinal displacement error, and heading angle, was reduced by about 32%, 25%, 37%, and 1%, respectively.

Day-ahead and real-time market bidding and scheduling strategy for wind power participation based on shared energy storage

• Day-ahead and real-time market bidding and scheduling strategy for wind power participation. • Shared energy storage is used to reduce the real-time market deviation penalty of wind power. • Analyze the influence of deviation penalty coefficient on wind power bidding. • Analyze the impact of energy storage capacity on the income of wind farms and energy storage operator. In order to reduce the impact of wind power output and electricity price uncertainty on the income of wind power participating in the electricity market, this paper proposes a day-ahead and real-time market bidding and scheduling strategy for wind power participation based on shared energy storage. In the first stage, considering the uncertainty of wind power output and electricity price, aiming at the maximum income of wind farms in the day-ahead market, the optimal bidding power of each wind farm in the day-ahead market is obtained by using quantum genetic algorithm. In the second stage, based on the day-ahead market winning bid volume and actual output, by coordinating each wind farm to use the charging and discharging services of the shared energy storage power station, the net income of multiple wind farms in the day-ahead and real-time market is maximized. Case studies are carried out with three wind farms and one shared energy storage operator in this paper. Compared with the modes that wind farms not configuring energy storage or configuring energy storage independently, it is verified that the shared energy storage can significantly reduce the deviation penalty of wind farms in the real-time market and improve wind farm income.

Combination Balance Correction of Grinding Disk Based on Improved Quantum Genetic Algorithm

To solve the problems of low measurement efficiency and measurement accuracy error in the balance correction of the mill before delivery. The measurement method of measuring a single chip instead of the whole chip group is adopted. And based on that, an improved adaptive quantum genetic algorithm (QGA) based on Bloch spherical coordinates is proposed. Three layers of quantum coding are used for the grinding group, the first layer adopts three-strand gene coding, the second and third layers adopt double-strand gene coding, and the adaptive step coefficient is introduced on this basis. The improved algorithm not only has the global search ability of a genetic algorithm (GA) but also has the coding ability of quantum bit, which can avoid the premature algorithm and improve the convergence performance of the algorithm. The results show that the proposed method enhances the correction accuracy compared with the traditional correction method. The residual unbalance is less than 0.005 kg; work efficiency increased by 92%. It can improve the calibration efficiency and accuracy of grinding disk installation balance measurement.

Quantum Genetic Algorithm for Highly Constrained Optimization Problems

Quantum computing appears as an alternative solution for solving computationally intractable problems. This paper presents a new constrained quantum genetic algorithm designed specifically for identifying the extreme value of a highly constrained optimization problem, where the search space size _database is massive and unsorted_ cannot be handled by the currently available classical or quantum processor, called the highly constrained quantum genetic algorithm (HCQGA). To validate the efficiency of the suggested quantum method, maximizing the energy efficiency with respect to the target user bit rate of an uplink multi-cell massive multiple-input and multiple- output (MIMO) system is considered as an application. Simulation results demonstrate that the proposed HCQGA converges rapidly to the optimum solution compared with its classical benchmark.

DIP-QGA: a secure and robust watermarking technique based on direct image projection and quantum genetic algorithm

DIP-QGA: a secure and robust watermarking technique based on direct image projection and quantum genetic algorithm

Optimal Orbit Design and Mission Scheduling for Sun-Synchronous Orbit On-orbit Refueling System

This paper proposes a two-stage optimization framework to simultaneously solve the orbit design and mission scheduling problems of on-orbit refueling (OOR) system in Sun-Synchronous Orbit (SSO). The fuel can be delivered to the client satellite (CS) by small-scale service satellites from the on-orbit fuel station (FS), which leads to a new FS-servicer-CS OOR mode. For the first stage, a modified spectral clustering-nonlinear programming (MSC-NLP) method is used to assign CSs to FSs, and design the orbit of FSs under <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">J</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> perturbation. For the second stage, the mission scheduling problem is formulated as a mixed-integer nonlinear programming (MINLP) model and solved via the genetic quantum algorithm (GQA). Different from the existing literature, this paper introduces a clustering distance metric combining multiple orbit characteristics for CS assignment, which can indirectly reduce the estimated fuel costs in the FS orbit design. Furthermore, the objective of reducing the number of servicers is first considered in the second stage. The effectiveness and superiority of the proposed two-stage framework are verified through several numerical simulations and comparison studies.

PID Controller for PMSM Speed Control Based on Improved Quantum Genetic Algorithm Optimization

When traditional proportional integral and differential controllers are applied to speed control in permanent magnet synchronous motors(PMSM), their coefficients are basically determined based on experience, which inevitably leads to unsatisfactory results when using this parameter to control the speed stability of permanent magnet synchronous motors. Therefore, this paper proposes an improved quantum genetic algorithm using quantum states as the basic unit. Utilizing quantum properties for global optimization to optimize the coefficients of proportional integral and differential control, improving the rotation angle of quantum state particles through the idea of velocity changes in particle swarm optimization(PSO), introducing adaptive weight changes, using Hadamard gates to replace traditional algorithm mutation strategies, and incorporating disaster mechanisms. In addition, this paper uses four test functions to find the minimum value, thereby verifying that our algorithm has better performance in optimization iteration compared to other algorithms, providing the initial basis for the next step of application in PID parameter optimization. Prove that this method can solve the problem of traditional genetic algorithms falling into local optima due to improper selection, crossover, and mutation methods, which cannot effectively control the stability of motor speed. Finally, this paper uses Matlab2018a simulation to compare with the other four algorithms, and the results show that this algorithm can find better PID parameter values to achieve better results in motor oscillation, overshoot, and faster target speed.

Green maritime: An improved quantum genetic algorithm-based ship speed optimization method considering various emission reduction regulations and strategies

Different organizations and countries have proposed a variety of policies and strategies to reduce emissions from the shipping industry. These include the Emission Control Area (ECA) policy, carbon tax policy, Vessel Speed Reduction Incentive Program (VSRIP), and Virtual Arrival (VA) strategy. These policies and strategies create additional costs for ships, forcing the ships to change their speeds or routes. This highlights the need to study approaches that optimize the sailing speeds of ships, by coordinating the implementation of the multiple governing policies and strategies. This paper develops a cost model that considers different emission reduction policies and strategies; this is followed by an analysis of their effect on emissions reduction. In addition, the paper proposes an improved quantum genetic algorithm based on a “fork gene” and elite strategy, which is specifically designed for specific shipping-related policies and strategy. The model is applied to a shipping route on the United States (US) West Coast, with three key findings. First, the improved algorithm significantly reduces the total cost, compared with the original alternative, in the context of emission reduction policies and strategies. Second, a single policy can produce positive results in reducing emissions, and the coordinated implementation of three separate policies (ECA, carbon tax, and VSRIP) may not lead to better results than a single policy. Finally, adding the VA strategy above and beyond the three other emission reduction policies may create more benefits in reducing emissions and operating costs.

Adaptive inverse optimal backstepping control strategy for longitudinal vibration of high-speed elevator system based on fuzzy observer

To effectively suppress the longitudinal vibration of the car under the conditions of high-speed operation and emergency braking, and improve the ride comfort of the car system, this paper proposes an adaptive fuzzy inverse optimal output feedback control strategy. Firstly, the dynamic model of the high-speed elevator system is established and the nonlinear dynamic model is approximated by the fuzzy logic system, and the auxiliary system model is established. Fuzzy state observer is designed to estimate the unmeasurable state. Furthermore, an adaptive inverse optimal output feedback controller based on fuzzy observer is designed by using adaptive backstepping technology and inverse optimal control principle. The stability analysis shows that the proposed adaptive fuzzy inverse optimal output feedback control strategy not only ensures the stability of the car attitude of high-speed elevator but also realizes the inverse optimization of the target cost function. Finally, the acceleration time–frequency response analysis of the two typical stages of high-speed elevator uniform running and emergency braking is carried out, and the numerical results are compared with the linear quadratic controller optimized by stepping quantum genetic algorithm (GA-LQR) and controller based on the state-dependent Ricatti equation (SDRE). The analysis verifies the effectiveness of the controller.

Utilizing quantum genetic algorithm with TM to solve DEs

In this paper a proposed approach to solve the DEs utilizing the TM with quantum Genetic algorithms. The aim of the proposed approach is to use the series functions of form quantum genetic to solve the DEs by decreasing the number of iterations and more increase by utilizing automata more with enhancement of TM.

Control strategy research of electric vehicle thermal management system based on MGA-SVR algorithm

The thermal management system is one of the important assemblies that ensure the secure operation of electric vehicles (EVs). Using intelligent algorithms to optimize the control strategy of the thermal management system can reduce energy consumption under the premise of effective heat dissipation of EVs. This paper attempts to construct the control strategy of EV thermal management system by coupling the modified genetic algorithm (MGA) and support vector regression (SVR). Firstly, the double-population adaptive mutation method and a novel optimization process are adopted to obtain MGA. Afterward, the performance of MGA is verified by four benchmark functions compared with three typical algorithms, which are genetic algorithm (GA), double-population genetic algorithm (DPGA), and quantum genetic algorithm (QGA). The results demonstrate that the accuracy and stability of MGA are obviously better than the other three algorithms. Secondly, MGA is applied to modify parameters of SVR kernel function, and the accuracy of MGA-SVR algorithm is verified by the Auto-MPG and Computer Hardware data sets. The mean square deviations of the SVR algorithm test set are 0.0186 and 0.0806, respectively, and the mean square deviations of the MGA-SVR algorithm test set are 0.0099 and 0.0054, respectively, which fully shows that MGA-SVR have more accurate forecasting capabilities. Finally, the thermal management system model of EV is built by the one-dimensional simulation software KULI. Under the Chinese working condition, fan speed which meets the cooling requirements of the motor and controller is obtained from the KULI model, and the database is constructed. Then, MGA-SVR is trained by database and employed to predict fan speed under the Chinese working condition and obtain control strategy of the thermal management system. Compared with traditional control strategy, the thermal management system based on MGA-SVR control strategy can not only meet the radiating requirements, but also effectively reduce the power consumption of fans.

A Modified Quantum-Inspired Genetic Algorithm for Continuum Structural Topology Optimization

Topology optimization and quantum computing have evolved rapidly over the past three decades. Previous topological optimum design methods suffered from financial burden and mathematical complexity. To overcome these shortcomings, a modified quantum-inspired evolutionary algorithm-based topology optimization method is proposed. This nested approach combines the classic solid isotropic microstructure with the penalization method and the double chains quantum genetic algorithm to establish an integral topology optimization framework. The former is utilized to determine the search direction of design variable updating. Meanwhile, the latter ensures abundant search diversity. The validity and feasibility of the developed methodology are eventually demonstrated by several application examples. The results indicate that the proposed optimization framework is independent of initial values and can lead to optimized structures. In addition, it will be more appropriate and effective if this strategy is deployed on a quantum computer in the future.

RSU Cluster Deployment and Collaboration Storage of IoV Based Blockchain

The Internet of Vehicles is increasingly becoming an indispensable platform to make interactions among vehicles, humans, and roadside infrastructures, and it is continuing to evolve with improvements on its application scenarios and service robustness. Academia and industry have also been concerned with the security issue of Internet of Vehicles (IoV). The Blockchain technology provides a new solution to improve security of the IoV, and it has drawn increased attention. Blockchain technology deals with the network model, identity authentication, trust management, and access control. However, there are insufficient studies on strategy of nodes deployment in the existing BIoV (Blockchain-based IoV). Based on the principle of partition, this paper studies the Blockchain-based IoV model in which the vehicle network system consists of vehicles and Road Side Unit (RSU) nodes. The Blockchain network is formed by the RSU nodes. By optimizing the LEACH algorithm, we partition RSUs and select cluster heads in BIoV, which has good scalability while maintaining a reasonable scale of Blockchain. Simulation results indicated that the improved-LEACH algorithm (I-LEACH) is more effective than the LEACH, DEEC, and the improved-DEEC algorithms (I-DEEC) with respect to the network life cycle and data transmission. Additionally, in order to reduce dependence of the storage resources from BIoV, we designed an intra-cluster collaborative storage scheme by adopting three algorithms for comparison—the genetic algorithm (GA), particle swarm optimization (PSO) algorithm, and the model quantum genetic algorithm (QGA). It revealed that the intra-cluster collaborative storage scheme is effective to ease the bottleneck of storage space demanded in BIoV, and verified by the simulation experiments.

A New Method for Finding the Shape of the Main Cable in the Special Cable Plane

The main cable and suspender of the spatial special-shaped suspension bridge are in the state of spatial stress; the structural stress is more complex than that of the parallel cable plane and finding the shape of the main cable of the spatial special-shaped cable plane is more difficult. In order to solve the problem of finding the shape of the main cable of the special-shaped suspension bridge, a new calculation model and algorithm of the main cable are proposed in this paper. The new calculation model adds constraints on the transverse bridge direction coordinates of the midspan on the basis of the original calculation model. The new calculation model can timely correct the calculation errors in all directions of the control node during the main cable shape finding process and improve the calculation accuracy. The new algorithm is a hybrid algorithm. The algorithm first uses the modified quantum genetic algorithm to solve and calculate the cable end force close to the real value, and then uses the cable end force obtained by the modified quantum genetic algorithm as the initial value to iterate through the modified least squares method. In this paper, a single cable plane curved suspension bridge is taken as the research background, and the differences of different calculation models in the shape-finding calculation of the main cable of the spatial special-shaped cable plane are compared. The results show that the proposed model is more stable in the calculation process, and the proposed algorithm has high accuracy and strong adaptability.

Development of Evolutionary Systems Based on Quantum Petri Nets

Evolutionary systems (ES) include software applications that solve problems using heuristic methods instead of the deterministic ones. The classical computing used for ES development involves random methods to improve different kinds of genomes. The mappings of these genomes lead to individuals that correspond to the searched solutions. The individual evaluations by simulations serve for the improvement of their genotypes. Quantum computations, unlike the classical computations, can describe and simulate a large set of individuals simultaneously. This feature is used to diminish the time for finding the solutions. Quantum Petri Nets (QPNs) can model dynamical systems with probabilistic features that make them appropriate for the development of ES. Some examples of ES applications using the QPNs are given to show the benefits of the current approach. The current research solves quantum evolutionary problems using quantum genetic algorithms conceived and improved based on QPN. They were tested on a dynamic system using a Quantum Discrete Controlled Walker (QDCW).

An Optimization Strategy of Composite Structure for Underwater Sound-Absorbing via Combining Response Surface Method and Quantum Genetic Algorithm

An Optimization Strategy of Composite Structure for Underwater Sound-Absorbing via Combining Response Surface Method and Quantum Genetic Algorithm

Dynamic Economic Dispatching Considering Time-Coupling Spinning Reserve Response Risk with High Penetration of Wind Power

Aiming at the problem that the current dynamic economic dispatch (DED) fails to consider the response risk of spinning reserve caused by the fluctuation and uncertainty of wind power, we work out a DED problem considering time-coupling spinning reserve response risk while the stochasticity and variability arising from RESs are taken into consideration. The developed framwork unified the response risk of reserve caused by forced shutdown of the unit into the response risk caused by time coupling. The expected customer interruption cost (ECOST) and the expected abandoned wind cost considering this reserve response risk are added to the objective function. While seeking the minimum objective function, the system is automatically configured with suitable reserve to ensure the consistency of the system’s response risk in each period. An improved multi-universe parallel quantum genetic algorithm was used to solve the model. Numerical examples and analysis prove the effectiveness and feasibility of the proposed method.

Fatigue Detection of Air Traffic Controllers Based on Radiotelephony Communications and Self-Adaption Quantum Genetic Algorithm Optimization Ensemble Learning

Air traffic controller (ATC) fatigue has become a major cause of air traffic accidents. Speech-based fatigue-state detection is proposed in this paper. The speech signal is preprocessed to further extract the Mel frequency cepstrum coefficient (MFCC) from speech discourse. The machine learning method is used in fatigue detection. However, single machine learning fatigue detection methods often have low detection accuracy. To solve this problem, an ensemble learning method based on self-adaption quantum genetic algorithm (SQGA) heterogeneous learning methods is proposed. Pattern-level and feature-level resampling are used to increase the differences in the base learner’s training dataset. To enlarge the diversity of single learners, k-nearest neighbor (KNN), Bayesian network (BN), back propagation neural network (BPNN) and support vector machine (SVM) are adopted for the heterogeneous ensemble. On this basis, finally, the detection result is obtained by weighted summation. The weight of each base learner was determined by SQGA. The SQGA method combines the quantum genetic algorithm with the adaptive strategy. The adaptive strategy includes adaptive adjustment of the quantum rotation gate, adaptive generation of crossover probability and adaptive generation of mutation probability. The experiments on real civil aviation radio land–air communication show that the proposed method can obtain 98.5% detection accuracy, with a 1.2% false and 3.0% missing report rate, whereas the SVM only obtains 94.0% detection accuracy, with a 5.4% false and 9.0% missing report rate.

Facial Recognition System Based on Genetic Algorithm Improved ROI-KNN Convolutional Neural Network

The facial recognition system is an application tool that uses artificial intelligence technology and biometrics technology to analyze and recognize the facial feature information of the human face. It is widely used in various fields, such as attendance and access control management in schools and companies, identity monitoring in stations and stores, facial recognition for fugitive criminals, and facial payment on mobile terminals. However, due to the short development time of the facial recognition system, the facial recognition system has the problem of low recognition accuracy when the recognized object is not cooperative. Although some scholars have proposed the region of interest (ROI)-K nearest neighbor algorithm (KNN) convolutional neural network theory by using the ROI and KNN and applied it to face recognition, the facial recognition system based on ROI-KNN convolutional neural network did not solve the problems of insufficient facial recognition accuracy and insufficient security. Under the conditions of insufficient illumination, excessive expression change, occlusion, high similarity of different individuals, and dynamic recognition, the recognition effect of the facial recognition system based on the ROI-KNN convolutional neural network is relatively limited. Therefore, to make the recognition accuracy of the facial recognition system higher and to make the facial recognition system play a greater role in the social and economic fields, this paper used the adaptive quantum genetic algorithm, the improved marker line graph genetic algorithm, and the feature weight value genetic algorithm to study the facial recognition system of the ROI-KNN convolutional neural network. The research results showed that after improving the ROI-KNN convolutional neural network based on the genetic algorithm, the recognition accuracy of the facial recognition system was increased by 4.99%, the recognition speed was increased by 7.46%, and the recognition security was increased by 2.66%.