Quantum Genetic Algorithm Research Articles

Improved auto control ant colony optimization using lazy ant approach for grid scheduling problem

An auto controlled ant colony optimization algorithm controls the behavior of the ant colony algorithm automatically based on a priori heuristic. During the experimental study of auto controlled ACO algorithm on grid scheduling problem, it was observed that the induction of lazy ants not only reduces the time complexity of the algorithm but also produces better results on the given objectives. Lazy ants are basically a mutated version of active ants that remain alive till the fitter lazy ants are generated in the successive generations. This work presents an improved auto controlled ACO algorithm using the lazy ant concept. Performance study reveals the efficacy and the efficiency achieved by the proposed algorithm. A comparative study of the proposed method with some other recent meta-heuristics such as auto controlled ant colony optimization algorithm, genetic algorithm, quantum genetic algorithm, simulated annealing and particle swarm optimization for grid scheduling problem exhibits so.

Determining the Optimal Placement of Sensors on a Concrete Arch Dam Using a Quantum Genetic Algorithm

Structural modal identification has become increasingly important in health monitoring, fault diagnosis, vibration control, and dynamic analysis of engineering structures in recent years. Based on an analysis of traditional optimization algorithms, this paper proposes a novel sensor optimization criterion that combines the effective independence (EFI) method with the modal strain energy (MSE) method. Considering the complex structure and enormous degrees of freedom (DOFs) of modern concrete arch dam, a quantum genetic algorithm (QGA) is used to optimize the corresponding sensor network on the upstream surface of a dam. Finally, this study uses a specific concrete arch dam as an example and determines the optimal sensor placement using the proposed method. By comparing the results with the traditional optimization methods, the proposed method is shown to maximize the spatial intersection angle among the modal vectors of sensor network and can effectively resist ambient perturbations, which will make the identified modal parameters more precise.

Stock Selection Based on a Hybrid Quantitative Method

Quantitative stock selection has become a research hotspot in the field of investment decision. As the data mining technology becomes mature, quantitative stock selection has made great progress. From the perspective of value investment, this paper selects top 200 stocks of A share in terms of market value. With the random forest (RF), financial characteristic variables with significant impact on SVR are screened out. At the same time with quantum genetic algorithm (QGA) superior to the traditional genetic algorithm (GA), SVR parameters are deeply and dynamically sought for, so as to build the RF-QGA-SVR model for year-to-year stock ranking. The quantitative stock selection model is built, and the empirical analysis of its stock selection performance is conducted. The conclusion is as follows: 1) Optimizing SVR with QGA has higher precision than the traditional genetic algorithm, and is more excellent than the traditional GA optimization; 2) SVR after RF optimization of characteristic variables more significantly improves the accuracy of stock ranking and prediction; 3) In the stock ranking obtained from the RF-QGA-SVR model, the yields of top stock portfolios are much higher than the market benchmark yield. At the same time, the yields of the top 10 stock portfolios are the highest, and the top 30 stock portfolios are the most stable. This study has positive reference significance on quantitative stock selection in the field of quantitative investment.

Modeling and Optimization of Vehicle Suspension Employing a Nonlinear Fluid Inerter

An ideal inerter has been applied to various vibration engineering fields because of its superior vibration isolation performance. This paper proposes a new type of fluid inerter and analyzes the nonlinearities including friction and nonlinear damping force caused by the viscosity of fluid. The nonlinear model of fluid inerter is demonstrated by the experiments analysis. Furthermore, the full-car dynamic model involving the nonlinear fluid inerter is established. It has been detected that the performance of the vehicle suspension may be influenced by the nonlinearities of inerter. So, parameters of the suspension system including the spring stiffness and the damping coefficient are optimized by means of QGA (quantum genetic algorithm), which combines the genetic algorithm and quantum computing. Results indicate that, compared with the original nonlinear suspension system, the RMS (root-mean-square) of vertical body acceleration of optimized suspension has decreased by 9.0%, the RMS of pitch angular acceleration has decreased by 19.9%, and the RMS of roll angular acceleration has decreased by 9.6%.

Gas Concentration Prediction Based on the Measured Data of a Coal Mine Rescue Robot

The coal mine environment is complex and dangerous after gas accident; then a timely and effective rescue and relief work is necessary. Hence prediction of gas concentration in front of coal mine rescue robot is an important significance to ensure that the coal mine rescue robot carries out the exploration and search and rescue mission. In this paper, a gray neural network is proposed to predict the gas concentration 10 meters in front of the coal mine rescue robot based on the gas concentration, temperature, and wind speed of the current position and 1 meter in front. Subsequently the quantum genetic algorithm optimization gray neural network parameters of the gas concentration prediction method are proposed to get more accurate prediction of the gas concentration in the roadway. Experimental results show that a gray neural network optimized by the quantum genetic algorithm is more accurate for predicting the gas concentration. The overall prediction error is 9.12%, and the largest forecasting error is 11.36%; compared with gray neural network, the gas concentration prediction error increases by 55.23%. This means that the proposed method can better allow the coal mine rescue robot to accurately predict the gas concentration in the coal mine roadway.

Data Collection Method for Mobile Control Sink Node in Wireless Sensor Network Based on Compressive Sensing

The paper proposes a mobile control sink node data collection method in the wireless sensor network based on compressive sensing. This method, with regular track, selects the optimal data collection points in the monitoring area via the disc method, calcu-lates the shortest path by using the quantum genetic algorithm, and hence determines the data collection route. Simulation results show that this method has higher network throughput and better energy efficiency, capable of collecting a huge amount of data with balanced energy consumption in the network.

The Analysis of the Concrete Gravity Dam’s Foundation Uplift Pressure under the Function of Typhoon

How to evaluate the seepage safety status of the concrete gravity dam under the function of short-period heavy rainfall and the possible historical extreme reservoir water level during typhoon is an important issue considering the dam safety-monitoring. Based on analysis of the monitoring series of the foundation uplift pressure, this paper assumed the influential process of antecedent reservoir water level and rainfall as a process of normal distribution and introduced the mutation factor to reflect the uprush feature of uplift pressure under the function of high-influential typhoon. Moreover, the corresponding hysteresis days and influential days of the model are optimized with quantum genetic algorithm (QGA) to raise the fitting and prediction accuracy. It is verified that the new statistical model for fitting can obtain higher multiple correlation coefficient (0.972) compared with the traditional statistical model (0.925) and could also perfectly predict the uprush feature of the pressure during the typhoon, which is of certain theoretical and practical application value in the future.

A Framework for Satellite Image Enhancement Using Quantum Genetic and Weighted IHS+Wavelet Fusion Method

this paper examined the applicability of quantum genetic algorithms to solve optimization problems posed by satellite image enhancement techniques, particularly super-resolution, and fusion. We introduce a framework starting from reconstructing the higher-resolution panchromatic image by using the subpixel-shifts between a set of lower-resolution images (registration), then interpolation, restoration, till using the higher-resolution image in pan-sharpening a multispectral image by weighted IHS+Wavelet fusion technique. For successful super-resolution, accurate image registration should be achieved by optimal estimation of subpixel-shifts. Optimal-parameters blind restoration and interpolation should be performed for the optimal quality higher-resolution image. There is a trade-off between spatial and spectral enhancement in image fusion; it is difficult for the existing methods to do the best in both aspects. The objective here is to achieve all combined requirements with optimal fusion weights, and use the parameters constraints to direct the optimization process. QGA is used to estimate the optimal parameters needed for each mathematic model in this framework “Super-resolution and fusion.” The simulation results show that the QGA-based method can be used successfully to estimate automatically the approaching parameters which need the maximal accuracy, and achieve higher quality and efficient convergence rate more than the corresponding conventional GA-based and the classic computational methods.

Fuzzy based Quantum Genetic Algorithm for Project Team Formation

Formation of an effective project team plays an important role in successful completion of the projects in organizations. As the computation involved in this task grows exponentially with the growth in the size of personnel, manual implementation is of no use. Decision support systems (DSS) developed by specialized consultants help large organizations in personnel selection process. Since, the given problem can be modelled as a combinatorial optimization problem, Genetic Algorithmic approach is preferred in building the decision making software. Fuzzy descriptors are being used to facilitate the flexible requirement specifications that indicates required team member skills. The Quantum Walk based Genetic Algorithm (QWGA) is proposed in this paper to identify near optimal teams that optimizes the fuzzy criteria obtained from the initial team requirements. Efficiency of the proposed design is tested on a variety of artificially constructed instances. The results prove that the proposed optimization algorithm is practical and effective.

An Allele Real-Coded Quantum Evolutionary Algorithm Based on Hybrid Updating Strategy.

For improving convergence rate and preventing prematurity in quantum evolutionary algorithm, an allele real-coded quantum evolutionary algorithm based on hybrid updating strategy is presented. The real variables are coded with probability superposition of allele. A hybrid updating strategy balancing the global search and local search is presented in which the superior allele is defined. On the basis of superior allele and inferior allele, a guided evolutionary process as well as updating allele with variable scale contraction is adopted. And H ε gate is introduced to prevent prematurity. Furthermore, the global convergence of proposed algorithm is proved by Markov chain. Finally, the proposed algorithm is compared with genetic algorithm, quantum evolutionary algorithm, and double chains quantum genetic algorithm in solving continuous optimization problem, and the experimental results verify the advantages on convergence rate and search accuracy.

A Nonlinear Blind Source Separation Method Based On Radial Basis Function and Quantum Genetic Algorithm

Blind source separation is a hot topic in signal processing. Most existing works focus on dealing with linear combined signals, while in practice we always encounter with nonlinear mixed signals. To address the problem of nonlinear source separation, in this paper we propose a novel algorithm using radial basis function neutral network, optimized by multi-universe parallel quantum genetic algorithm. Experiments show the efficiency of the proposed method.

The Combination of Extension of Ant Colony Algorithm and Other Intelligent Algorithms

The extension of ant colony algorithm was proposed by Dorigo, the founder of ant colony algorithm, which is the latest ant colony algorithm for solving a continuous space optimization problem. Considering the blindness of man-made choice of initial solution and initial parameters of the algorithm, and according to the algorithm converging slowly and easily falling into local optimum, this paper has provided improvement strategy for this optimization. It has introduced quantum computing and genetic algorithm, chaos optimization to carry out combination and comparison, and it has carried out improvement on the weight internally solved by memory in the algorithm. The effectiveness of various combined algorithms was determined through the optimization of numerous multi-dimensional continuous functions.

An effective co-evolutionary quantum genetic algorithm for the no-wait flow shop scheduling problem

This article proposes a competitive co-evolutionary quantum genetic algorithm for the no-wait flow shop scheduling problem with the criterion to minimize makespan, which is a renowned NP-hard combinatorial optimization problem. An innovative coding and decoding mechanism is proposed. The mechanism uses square matrix to represent the quantum individual and adapts the quantum rotation gate to update the quantum individual. In the algorithm framework, the store-with-diversity is proposed to maintain the diversity of the population. Moreover, a competitive co-evolution strategy is introduced to enhance the evolutionary pressure and accelerate the convergence. The store-with-diversity and competitive co-evolution are designed to keep a balance between exploration and exploitation. Simulations based on a benchmark set and comparisons with several existing algorithms demonstrate the effectiveness and robustness of the proposed algorithm.

Parameter Optimization Based on Evolutionary Algorithms for Green Cognitive Radio

In this paper, we study parameter adjustments that maximize the energy efficiency of green cognitive radio. Because the problem of parameter adjustments for green cognitive radio can be looked at as a complex discrete optimization problem, evolutionary algorithms can be applied to solve it. Parameter optimization methods based on quantum genetic algorithm (QGA), particle swarm optimization (PSO), and chaotic quantum particle swarm optimization (CQPSO) are designed. In particular, CQPSO integrates the characteristics of chaos with quantum particle swarm optimization (QPSO), which gives it strong global search abilities. Chaotic mutation is introduced into the proposed CQPSO to avoid premature convergence and keep diversity in populations. Quantum computing has excellent features used to increase optimization speed and enhance the search abilities of the algorithm. The proposed CQPSO method provides good performance in terms of convergence rate and convergence accuracy, and can search for an optimal solution to parameter adjustments in green cognitive radio networks. Through simulations comparing it to QGA and PSO, we conclude that the proposed CQPSO can improve energy efficiency and meet users’ quality of service needs.

Computation of hyperfine energies of hydrogen, deuterium and tritium quantum dots

The hyperfine energies and hyperfine constants of the ground and excited states of hydrogen, deuterium and tritium quantum dots(QDs) are calculated. Quantum genetic algorithm (QGA) and Hartree–Fock–Roothaan (HFR) methods are employed to calculate the unperturbed wave functions and energy eigenvalues. The results show that in the medium and strong confinement regions the hyperfine energy and hyperfine constant are strongly affected by dot radius, impurity charge, electron spin orientation, impurity spin and impurity magnetic moment. Besides, in all dot radii, the hyperfine splitting and hyperfine constant of the confined hydrogen and tritium atoms are approximately equivalent to each other and they are greater than the confined deuterium atom.

The vehicle routing problem with multiple prioritized time windows: A case study

This paper addresses Multi-objective Vehicle Routing Problem with Multiple Prioritized Time Windows (VRPMPTW) in which the distributer proposes a set of all non-overlapping time windows with equal or different lengths and the customers prioritize these delivery time windows. VRPMPTW aims to find a set of routes of minimal total traveling cost and maximal customer satisfaction (with regard to the prioritized time windows), starting and ending at the depot, in such a way that each customer is visited by one vehicle given the capacity of the vehicle to satisfy a specific demand. This problem is inspired from a real life application. The contribution of this paper lies in its addressing the VRPMPTW from a problem definition, modeling and methodological point of view. We developed a mathematical model for this problem. This model can simply be used for a wide range of applications where the customers have multiple flexible time windows and violation of time windows may drop the satisfaction levels of customers and lead to profit loss in the long term. A Cooperative Coevolutionary Multi-objective Quantum-Genetic Algorithm (CCMQGA) is also proposed to solve this problem. A new local search is designed and used in CCMQGA to reach an appropriate pareto front. Finally, the proposed approach is employed in a real case study and the results of the proposed CCMQGA are compared with the current solution obtained from managerial experience, the results of NSGA-II and the multi-objective quantum-inspired evolutionary algorithm.

Study on Driver Model Parameters Distribution for Fatigue Driving Levels Based on Quantum Genetic Algorithm

According to the characteristics that fatigue study cannot reveal fatigue mechanism and nonlinear influence factors of vehicle driving closed-loop system defects, this paper proposes a driver model inversion method for studying the driver's fatigue diagnosis. Furthermore, the new methods is divided into two steps: 1. using the forecast looks neural network model to build the driver vehicle road closed loop model which is adapted to the complex road conditions. Besides, and the model was used to study the car system parameter changes of the closed-loop system in which the driver is in a state of fatigue. 2. By defining specific movement track in the degree of approximation of theoretical data and taking test data as the objective function, we take the driver parameter inverse problem into multiple target optimization problems. Using a method of real-coded chaotic mutation of quantum genetic algorithm (GA) optimization to obtain the global optimal solution. The driving simulation test results show that under the condition of complex road conditions, the proposed algorithm in actual driving parameter inversion of the alignment is superior to the traditional genetic algorithm (GA) and the traditional quantum genetic algorithm (QGA), Finally the pilot model parameters the relationship between fatigue factors are made.

Acoustic partial discharge localization methodology in power transformers employing the quantum genetic algorithm

Partial discharge (PD) is one of staple reasons resulting in the deterioration of insulation system in power transformers and power faults. If the PD activity can be located at an early stage, preventive maintenance measures can be taken effectively. Therefore, the localization techniques of the PD do play an extraordinarily momentous role to enhance the operating reliability and stability of the system. It is proposed that a methodology based on the quantum genetic algorithm (QGA) adopting acoustic emission (AE) techniques for locating PD in power transformers. Results of the calculating example confirm the localization effectiveness and accurateness of the proposed method are ideal and satisfactory, and that precision of localization is enhanced compared to some other localization algorithms. The acoustic localization methodology based on the QGA is feasible and of suitability for the field applications.

Quantum Inspired GA based Neural Control of Inverted Pendulum

paper deals with comparison of artificial neural network and quantum inspired evolutionary neural network control of an inverted pendulum. First, a properly tuned PID controller was utilized to stabilize the inverted pendulum to generate the training data. Secondly, a feed-forward neural network was trained on the basis of these data. Thirdly, a quantum genetic algorithm optimized neural network was developed. If a disturbance occurs in the system, the controllers counteract this disturbance and balance inverted pendulum. All these three schemes are tested and compared. The results establish that the quantum genetic algorithm neural controller has the best control action.

Calculation of hyperfine interaction in spherical quantum dot

In this study, we have calculated the unperturbed wavefunctions and energy eigenvalues of the ground and excited states of a spherical quantum dot, GaAs/AlxGa1−xAs, by using quantum genetic algorithm and Hartree–Fock Roothaan method. Hyperfine coupling constant and hyperfine energy of 1s, 2p, 3d and 4f levels are carried out as a function of dot radius. The results show that the hyperfine constant and hyperfine energy varies rapidly in the strong and medium confining regions as dot radius decreases. It is worth pointing out that dot radius, impurity charge and angular momentum have a strong influence on the hyperfine energy. It is also found that hyperfine energy and hyperfine splitting vary with aluminium concentration ratio x.