Double Chains Quantum Genetic Algorithm Research Articles

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.

Quantum SUSAN edge detection based on double chains quantum genetic algorithm

Edge detection algorithm based on quantum image processing has attracted much attention due to low circuit complexity and small storage capacity. Since the classical smallest univalue segment assimilating nucleus (SUSAN) algorithm is limited in vertical and horizontal directions, a new quantum SUSAN edge detection scheme based on double chains quantum genetic algorithm is designed. First, the linear X-shift and the Y-shift transforms are raised to accelerate the qubit retrieval process and output the quantum feature information. Then, the quantum feature information is fed to quantum comparator and surround suppression circuit exports gradient information. Finally, the quantum SUSAN classifier outputs classification results. The presented algorithm combines the advantages of SUSAN’s accurate positioning and texture edge suppression with the superposition of the quantum genetic algorithm, which avoids the trap of local optimum. In the quantum SUSAN classifier circuit, the double chains quantum genetic coding and the circular SUSAN mask are introduced to improve classification accuracy. Experimental results verify that the proposed scheme has a good edge searchability. Moreover, the edge points obtained by this algorithm are more integral and continuous than other classical algorithms and existing quantum algorithms.

A quantum artificial neural network for stock closing price prediction

In practice, stock market behavior is difficult to predict accurately because of its high volatility. To improve market forecasts, a method inspired by Elman neural network and quantum mechanics is presented. To render the network sensitive to dynamic information, the internal self-connection signal that is extremely useful for system modeling is introduced to the proposed technique. Double chains quantum genetic algorithm is employed to tune the learning rates. This model is validated by forecasting closing prices of six stock markets, the simulation results indicate that the proposed algorithm is feasible and effective. Accordingly, generalizing the method is deemed advantageous.

Study on intelligent prediction method of rock drillability based on Bayesian lithology classification and optimized BP neural network

With the rapid development of shale gas in northeastern Sichuan province, problems such as low drilling rate, long drilling cycle and serious bit wear are becoming more and more prominent in shale gas drilling process. This paper proposes new prediction theory with two steps based on logging data sensitive to lithology by introducing Bayesian classifier and Self Adaptive Double Chain Quantum Genetic Algorithm (S-DC-QGA) which could optimize BP neural network prediction model. In the first step, the Bayesian network classifier achieved the classification and recognition of lithology, improved the correlation of model sample data, and enhanced the accuracy and speed of lithology classification. In the second step, S-DC-QGA characterized as the global optimization is used to optimize the number of hidden layer nodes, weights and thresholds of the BP neural network. Results showed that Bayesian lithologies classification enhanced the correlation of samples, moreover optimized BP neural network solved overfitting, random initial weights of BP itself and oscillation of fitting and generalization ability with slight change of network parameters, improving the prediction accuracy of rock drillability parameters and the generalization ability of the model. The findings of this study can help for better drilling design and improvement of mechanical drilling speed.

Transformer Health Management Based on Self-Powered RFID Sensor and Multiple Kernel RVM

Health management for transformer in service is of great significance to the reliable operation of power system. This paper presents a cost-efficient transformer condition prognosis methodology. First, a power management circuit is designed to guarantee the stable performance of self-powered RFID sensor. Moreover, the sensor data storage mechanism is modified by embedding the sensor data into the ID information of RFID sensor to realize less response time and lower power consumption compared with the traditional approach. Second, a denoising method based on band restricted empirical mode decomposition and a fuzzy threshold is introduced to extract noise-free vibration signal from the measured raw signal. Third, the multiple kernel relevance vector machine (MKRVM) is imported to predict the future condition of transformer. The kernels of MKRVM keep diversity for higher prediction accuracy by using the heterogeneous kernel learning approach. Moreover, the double chain quantum genetic algorithm is employed as optimization tool to find the optimal sparse weights of basic kernel functions in MKRVM. The experimental results indicate that the modified RFID sensor can stably operate even when the load of the transformer is abruptly decreased to zero. The response time and power consumption of RFID sensor are markedly decreased by using the improved sensor data storage mechanism. Furthermore, the prediction experiment results demonstrate that the proposed approach is effective in term of health monitoring for transformer.

Open Switch Fault Diagnosis Method for PWM Voltage Source Rectifier Based on Deep Learning Approach

With the development of machine learning technology, numerous studies have been proposed to diagnose the open circuit (OC) faults in the pulse width modulation (PWM) voltage source rectifier (VSR) systems. However, most methods require system signals of more than one current period, which show poor real-time performance. Aiming at this problem, this paper presents an improved diagnosis system based on deep belief networks (DBN) and least square support vector machine (LSSVM). First, the double chain quantum genetic algorithm (DCQGA) is employed to obtain the proper length of measured signals and DBN structure parameters. Then, the fault features are extracted from the signals through DBN. Finally, these features are used to train the LSSVM fault classifier to construct the diagnosis model. The experimental results show that the proposed method can achieve the fault diagnosis including six kinds of single switch faults and 15 kinds of different double switches faults correctly. Besides, the proposed method also shows the superior anti-interference performance and high robustness on abrupt load transient conditions, unbalanced, and/or distorted grid voltage conditions, as well as, different power factor conditions. Furthermore, the average diagnostic time of this method is only 2.57 ms.

A New Radar Signal Recognition Method Based on Optimal Classification Atom and IDCQGA

Radar electronic reconnaissance is an important part of modern and future electronic warfare systems and is the primary method to obtain non-cooperative intelligence information. As the task requirement of radar electronic reconnaissance, it is necessary to identify the non-cooperative signals from the mixed signals. However, with the complexity of battlefield electromagnetic environment, the performance of traditional recognition system is seriously affected. In this paper, a new recognition method based on optimal classification atom and improved double chains quantum genetic algorithm (IDCQGA) is researched, optimal classification atom is a new feature for radar signal recognition, IDCQGA with symmetric coding performance can be applied to the global optimization algorithm. The main contributions of this paper are as follows: Firstly, in order to measure the difference of multi-class signals, signal separation degree based on distance criterion is proposed and established according to the inter-class separability and intra-class aggregation of the signals. Then, an IDCQGA is proposed to select the best atom for classification under the constraint of distance criterion, and the inner product of the signal and the best atom for classification is taken as the eigenvector. Finally, the extreme learning machine (ELM) is introduced as classifier to complete the recognition of signals. Simulation results show that the proposed method can improve the recognition rate of multi-class signals and has better processing ability for overlapping eigenvector parameters.

A Framework of Joint Energy Provisioning and Manufacturing Scheduling in Smart Industrial Wireless Rechargeable Sensor Networks.

Energy provisioning is always a crucial problem restricting the further development and application of smart industrial wireless sensor networks in smart factories. In this paper, we present that it is necessary to develop smart industrial wireless rechargeable sensor networks (SIWRSNs) in a smart factory environment. Based on the complexity and time-effectiveness of factory operations, we establish a joint optimization framework named J-EPMS to effectively coordinate the charging strategies of wireless sensors and the scheduling plans of machines running. Then, we propose a novel double chains quantum genetic algorithm with Taboo search (DCQGA-TS) for J-EPMS to obtain a suboptimal solution. The simulation results demonstrate that the DCQGA-TS algorithm can maximally ensure the continuous manufacturing and markedly shorten the total completion time of all production tasks.

Tilt Angle On-Line Prognosis by Using Improved Sparse LSSVR and Dynamic Sliding Window

On-line tilt angle detection and prognosis are the important prerequisites of real-time movement controlling. This paper develops a time-effective tilt angle prognosis methodology and its evaluation prototype. First, a complementary filter is imported to extend both the superiorities sufficiently of accelerometer and gyroscope, accurate tilt angle detection then can be achieved in a wide range of frequency. Second, an improved sparse least-squares support vector regression is proposed for tilt angle prognosis, its configurable singularity spectrum technology keeps away from the high-order matrix operation through eliminating nonprincipal components among raw vectors. Third, novel composite judgment criteria and its derived dynamic sliding window mechanism are proposed to pursue scientific balance of the prediction precision and computational cost. Fourth, double chains quantum genetic algorithm is introduced as the optimization tool to search optimal parameters of the above-mentioned models. Finally, the prototype setup and the corresponding experimental results are demonstrated and discussed in detail, which promise that our proposed methodology could be potentially applied to the actual motion control systems.

An improved quantum genetic algorithm based on MAGTD for dynamic FJSP

For the purpose of solving the dynamic flexible job-shop scheduling problem, this paper establishes the mathematical model to minimize the makespan and stability value, an improved double chains quantum genetic algorithm was proposed. Firstly, it is proposed that the method of double chains structure coding including machine allocation chain and process chain. Secondly, it is proposed that non- dominated sorting based on the crowding distance selection strategy. Thirdly, the most satisfying solution is obtained through the multi-attribute grey target decision model. Finally, the novel method is applied to the Brandimarte example and a dynamic simulation, the result of comparing with other classical algorithms verifies its effectiveness.

Combining DCQGMP-Based Sparse Decomposition and MPDR Beamformer for Multi-Type Interferences Mitigation for GNSS Receivers.

In the coexistence of multiple types of interfering signals, the performance of interference suppression methods based on time and frequency domains is degraded seriously, and the technique using an antenna array requires a large enough size and huge hardware costs. To combat multi-type interferences better for GNSS receivers, this paper proposes a cascaded multi-type interferences mitigation method combining improved double chain quantum genetic matching pursuit (DCQGMP)-based sparse decomposition and an MPDR beamformer. The key idea behind the proposed method is that the multiple types of interfering signals can be excised by taking advantage of their sparse features in different domains. In the first stage, the single-tone (multi-tone) and linear chirp interfering signals are canceled by sparse decomposition according to their sparsity in the over-complete dictionary. In order to improve the timeliness of matching pursuit (MP)-based sparse decomposition, a DCQGMP is introduced by combining an improved double chain quantum genetic algorithm (DCQGA) and the MP algorithm, and the DCQGMP algorithm is extended to handle the multi-channel signals according to the correlation among the signals in different channels. In the second stage, the minimum power distortionless response (MPDR) beamformer is utilized to nullify the residuary interferences (e.g., wideband Gaussian noise interferences). Several simulation results show that the proposed method can not only improve the interference mitigation degree of freedom (DoF) of the array antenna, but also effectively deal with the interference arriving from the same direction with the GNSS signal, which can be sparse represented in the over-complete dictionary. Moreover, it does not bring serious distortions into the navigation signal.

Comprehensive evaluation of integrative drip-irrigation and fertilization using a projection pursuit model based on the improved double chains quantum genetic algorithm

Comprehensive evaluation of integrative drip-irrigation and fertilization using a projection pursuit model based on the improved double chains quantum genetic algorithm

An improved SVM classifier based on double chains quantum genetic algorithm and its application in analogue circuit diagnosis

Support Vector Machine (SVM) classifier is widely used in analogue circuit diagnosis. However, the penalty parameter C and the kernel parameter γ of SVM classifier with the radial basis function (RBF) affect the classification performance seriously. A double-chains-quantum-genetic-algorithm (DCQGA) based method is proposed to optimize C and γ. In DCQGA, each chromosome carries two gene chains, and each of gene chains represents an optimization solution, which can accelerate the search process and help to find the global solution. Thereafter, the optimal parameters C and γ are obtained by optimizing the parameter searching process with DCQGA. Two common datasets named Iris and Wine from UCI Machine Learning Repository are used to test the performance of the presented SVM classifier. The simulation results illustrate that the population's best fitness and the classifying accuracy of the proposed DCQGA-SVM are higher than that of the Particle-Swarm-Optimization based SVM (PSO-SVM), the Quantum Genetic Algorithm based SVM (QGA-SVM) and the classifier based on grid search method (GS-SVM). Finally, the proposed DCQGA-SVM is applied to analogue circuit diagnosis, a Sallen–Key bandpass filter circuit and a four-opamp biquad high-pass filter are chosen as circuits under test (CUT). Wavelet packet analysis is performed to extract the fault features before classifying. The experimental results show that the SVM parameters selected by DCQGA-SVM contribute to higher diagnosis accuracy than other methods referred in this paper.

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.

Adaptive double chain quantum genetic algorithm for constrained optimization problems

Optimization problems are often highly constrained and evolutionary algorithms (EAs) are effective methods to tackle this kind of problems. To further improve search efficiency and convergence rate of EAs, this paper presents an adaptive double chain quantum genetic algorithm (ADCQGA) for solving constrained optimization problems. ADCQGA makes use of double-individuals to represent solutions that are classified as feasible and infeasible solutions. Fitness (or evaluation) functions are defined for both types of solutions. Based on the fitness function, three types of step evolution (SE) are defined and utilized for judging evolutionary individuals. An adaptive rotation is proposed and used to facilitate updating individuals in different solutions. To further improve the search capability and convergence rate, ADCQGA utilizes an adaptive evolution process (AEP), adaptive mutation and replacement techniques. ADCQGA was first tested on a widely used benchmark function to illustrate the relationship between initial parameter values and the convergence rate/search capability. Then the proposed ADCQGA is successfully applied to solve other twelve benchmark functions and five well-known constrained engineering design problems. Multi-aircraft cooperative target allocation problem is a typical constrained optimization problem and requires efficient methods to tackle. Finally, ADCQGA is successfully applied to solving the target allocation problem.

The Evaluation of Dynamic Airport Competitiveness Based on IDCQGA-BP Algorithm

Aimed at the multidimensional and complex characteristic of airport competitiveness, a new algorithm is proposed in which BP neural network is optimized by improved double chains quantum genetic algorithm (IDCQGA-BP). The new algorithm is better than existing algorithms in convergence and the diversity of quantum chromosomes. The empirical data of eight airports in Yangtze River Delta in 2011 and 2012 is applied to verify the feasibility of the new algorithm, and then the competitiveness of the eight airports from 2013 to 2015 is gotten through the algorithm. The results show the following. (1) The new algorithm is better than the existing optimization algorithms in the aspects of error accuracy and run time. (2) The gaps of the airports in Yangtze River Delta are narrowing; the competition and cooperation are getting stronger and stronger. (3) The main increase reason of airport competitiveness is the increase of own investment.

Double chains quantum genetic algorithm with application to neuro-fuzzy controller design

This paper proposes a double chains quantum genetic algorithm (DCQGA), and shows its application in designing neuro-fuzzy controller. In this algorithm, the chromosomes are composed of qubits whose probability amplitudes comprise gene chains. The quantum chromosomes are evolved by quantum rotation gates, and mutated by quantum non-gates. For the direction of rotation angle of quantum rotation gates, a simple determining method is proposed. The magnitude of rotation angle is computed by integrating the gradient of the fitness function. Furthermore, a normalized neuro-fuzzy controller (NNFC) is constructed and designed automatically by the proposed algorithm. Application of the DCQGA-designed NNFC to real-time control of an inverted pendulum system is discussed. Experimental results demonstrate that the designed NNFC has very satisfactory performance.