Quantum Genetic Algorithm Research Articles

Assessing Chilling Injury in Cucumber Seedlings Using Chlorophyll Fluorescence Based on a Quantum Genetic Algorithm and Support Vector Regression Model

HighlightsA method is proposed to analyze chilling injury in cucumber seedlings.A mutual information method was adopted to determine model output.Different modeling approaches were compared for improving accuracy.The optimal model input type was determined and discussed.Abstract. Low temperature limits photosynthesis and leads to reduced production in plants. Accurate monitoring of the physiological state of cucumber (Cucumis sativus L.) seedlings and assessing their chilling injury are of great importance for the off-season cucumber industry. Dark chlorophyll fluorescence parameters have been proven to be efficient for revealing environmental stress on plant growth. We used seedlings of cucumber cultivar Bonai 14-3 to test a nested set of fluorescence parameters to assess the response characteristics of dark chlorophyll fluorescence parameters during eight days at low temperatures. Mutual information analysis showed that the dark chlorophyll fluorescence parameter Fv/Fo, an indicator of the potential activity of photosystem II, gave the strongest correlation with stage of chilling injury. We compared three Fv/Fo prediction models with different inputs, constructed using a quantum genetic algorithm support vector regression method. The model showed the highest accuracy when the inputs were Fv and Fo measured before low-temperature treatment, coupled with temperature, and duration. The coefficient of determination of the training and testing sets were both greater than 0.98. The root mean square error and the mean absolute error were both less than 0.15. This model was able to achieve accurate prediction of Fv/Fo for seedlings in a low-temperature environment, providing a potential method for non-destructive analysis and diagnosis of cucumber chilling injury. Keywords: Chlorophyll fluorescence parameters, Low temperature, Mutual information, Prediction.

Quantum Genetic Algorithm for Resource-Constrained Project Scheduling

Quantum Genetic Algorithm for Resource-Constrained Project Scheduling

Analysis of wind farm participation in frequency regulation considering multi-market interests

For wind farms to participate in the combined energy-frequency regulation (E-FR) market, wind farms are considered as a combination of both power generation and frequency regulation capability, and wind farms bid in both the energy market and the FR market. In this paper, the impact of different bidding decisions on the distribution of wind farm revenues is analyzed in a process where the interest of two markets is played against each other. A wind power probability density prediction model of kernel extreme learning machine (KELM)-particle swarm optimization (PSO)-adaptive diffusion kernel density estimation (AKDE) is established using an improved extreme learning machine (KELM) with good fitting ability and the AKDE method, wind farm bidding is carried out on the premise of wind power probability prediction, which is optimally solved by the multi-objective quantum genetic algorithm, and the optimization results are filtered using entropy-fuzzy C-means clustering. Based on the actual wind farm operation data for simulation analysis, the model analyzes the benefits of wind farm participation in the joint market from different preference perspectives, which is a reference for wind farm participation in bidding decisions.

Application of quantum genetic algorithm in high noise laser image security

Application of quantum genetic algorithm in high noise laser image security

3D-Container Loading Problem with a Distribution Plan Based on Hybrid Quantum Genetic Algorithm

3D-Container Loading Problem with a Distribution Plan Based on Hybrid Quantum Genetic Algorithm

A dynamic model for supercritical once-through circulating fluidized bed boiler-turbine units

It is urgent to establish a dynamic model of once-through supercritical circulating fluidized bed (SCFB) power generation units in order to explore the flexible operation mode. This study aims to establish such a dynamic model with the balance between model complexity and accuracy. Within the analysis on operation characteristics of SCFB units, some reasonable assumptions and simplifications were introduced. Then, a dynamic model structure was derived from mass, energy balance laws as well as thermodynamic principles. And the unknown parameters in the model were identified by regression analysis and quantum genetic algorithm, combined with running data from a 350 MW SCFB unit. After this, the open-loop step responses and closed-loop experiments were developed to further validate the established model. The simulation results show that all model outputs can track the dynamic trends of running data in different load conditions with the satisfactory accuracy. More importantly, the model captures the special dynamic characteristics of the SCFB unit. Therefore, the model can be feasible and applicable for advanced control algorithms test and flexible operation control system design of SCFB units.

Optimization of Network Coding Resources Based on Improved Quantum Genetic Algorithm

The problem of network coding resource optimization with a known topological structure is NP-hard. Traditional quantum genetic algorithms have the disadvantages of slow convergence and difficulty in finding the optimal solution when dealing with this problem. To overcome these disadvantages, this paper proposes an adaptive quantum genetic algorithm based on the cooperative mutation of gene number and fitness (GNF-QGA). This GNF-QGA adopts the rotation angle adaptive adjustment mechanism. To avoid excessive illegal individuals, an illegal solution adjustment mechanism is added to the GNF-QGA. A solid demonstration was provided that the proposed algorithm has a fast convergence speed and good optimization capability when solving network coding resource optimization problems.

Research on Fan Operation Evaluation and Error State Judgment Relying on Improved Neural Network and Intelligent Computing

Fan, as the most commonly used mechanical equipment, is widely used. In order to solve the problem of fan bearing fault diagnosis, this paper analyzes the main factors affecting fan spindle speed and power generation in operation. The input and output parameters of the performance prediction model are determined. The performance prediction model of wind turbine is established by using generalized regression neural network, and the smoothing factor of GRNN is optimized by comparing the prediction accuracy of the model. Based on this model, the sliding data window method is used to calculate the residual evaluation index of wind turbine speed and power in real time. When the evaluation index continuously exceeds the pre-set threshold, the abnormal state of wind turbine can be judged. In order to obtain wind turbine blades with better aerodynamic performance, a blade aerodynamic performance optimization method based on quantum heredity is proposed. The B é zier curve control point is used as the design variable to represent the continuous chord length and torsion angle distribution of the blade, the blade shape optimization model aiming at the maximum power is established, and the quantum genetic algorithm is used to optimize the chord length and torsion angle of the blade under different constraints. The optimization results of quantum genetic algorithm and classical genetic algorithm are compared and analyzed. Under the same parameters and boundary conditions, the proposed blade aerodynamic optimization method based on quantum genetic optimization is better than the classical genetic optimization method, and can obtain better blade aerodynamic shape and higher wind energy capture efficiency. This method makes up for the shortcomings of traditional fault diagnosis methods, improves the recognition rate of fault types and the accuracy of fault diagnosis, and the diagnosis effect is good.

Research on short term prediction method of thermal hydraulic transient operation parameters based on automated deep learning

Automated deep learning is a new development direction of deep learning based on automated machine learning. Its main purpose is to achieve the automated operation of deep learning algorithm design process from design, training, parameter adjustment and so on. Due to a large number of repeated operations such as parameter adjustment and optimization model, the algorithm design process relies heavily on designers and consumes a lot of design time. Based on the automated deep learning method, this paper designs a short term prediction method of thermal hydraulic transient operation parameters, which can achieve the whole design process hosting operation. This method only needs the designer to upload the data set to the prediction model to complete the algorithm design, and does not need the designer’s intervention in the whole process, so as to simplify the operation and save the design time. At the same time, quantum genetic algorithm and model agnostic meta learning are introduced to ensure that the algorithm is still reliable and effective when the data set is expanded and the prediction problem is more complex. And the algorithm can be porting and application on quantum computer. In order to verify the algorithm, the steam mass flow rate at the outlet of main steam pipe of steam generator and the water temperature at the bottom of pressurizer are predicted respectively after 1 s, 2 s, 3 s and 5 s. The calculations show that the maximum error of the prediction models is about 4%, and the average prediction time of a group of parameters is about 0.7 ms. The prediction model based on the combination of the above algorithms can be used to predict the change trend of thermal transient operation parameters in a short time and provide effective judgment basis for the reactor accident early warning.

A Dynamic Service Reconfiguration Method for Satellite–Terrestrial Integrated Networks

Satellite–terrestrial integrated networks (STINs) are regarded as a promising solution to meeting the demands of global high-speed seamless network access in the future. Software-defined networking and network function virtualization (SDN/NFV) are two complementary technologies that can be used to ensure that the heterogeneous resources in STINs can be easily managed and deployed. Considering the dual mobility of satellites and ubiquitous users, along with the dynamic requirements of user requests and network resource states, it is challenging to maintain service continuity and high QoE performance in STINs. Thus, we investigate the service migration and reconfiguration scheme, which are of great significance to the guarantee of continuous service provisioning. Specifically, this paper proposes a dynamic service reconfiguration method that can support flexible service configurations on integrated networks, including LEO satellites and ground nodes. We first model the migration cost as an extra delay incurred by service migration and reconfiguration and then formulate the selection processes of the location and migration paths of virtual network functions (VNFs) as an integer linear programming (ILP) optimization problem. Then, we propose a fuzzy logic and quantum genetic algorithm (FQGA) to obtain an approximate optimal solution that can accelerate the solving process efficiently with the benefits of the high-performance computing capacity of QGA. The simulation results validate the effectiveness and improved performance of the scheme proposed in this paper.

A Joint Optimization Framework of the Embedding Model and Classifier for Meta-Learning

The aim of meta-learning is to train the machine to learn quickly and accurately. Improving the performance of the meta-learning model is important in solving the problem of small samples and in achieving general artificial intelligence. A meta-learning method based on feature embedding that exhibits good performance on the few-shot problem was previously proposed. In this method, the pretrained deep convolution neural network was used as the embedding model of sample features, and the output of one layer was used as the feature representation of samples. The main limitation of the method is the inability to fuse low-level texture features and high-level semantic features of the embedding model and joint optimization of the embedding model and classifier. Therefore, a multilayer adaptive joint training and optimization method of the embedding model was proposed in the current study. The main characteristics of the current method include using multilayer adaptive hierarchical loss to train the embedding model and using the quantum genetic algorithm to jointly optimize the embedding model and classifier. Validation was performed based on multiple public datasets for meta-learning model testing. The proposed method shows higher accuracy compared with multiple baseline methods.

Dynamic modeling for SO2-NOx emission concentration of circulating fluidized bed units based on quantum genetic algorithm - Extreme learning machine

To meet the stringent emission standards and achieve cleaner production of circulating fluidized bed units, it is necessary to build a dynamic model of pollutants emission for creating an economical and environmentally friendly pollutant removal operation mode. This article fources on the modeling and accurate prediction of SO2-NOx emission concentration of the circulating fluidized bed unit. According to the generation and reduction mechanism of pollutants, the model inputs are selected and determined by Pearson coefficient. Then, a dynamic model of SO2-NOx emission concentration based on extreme learning machine is developed, and quantum genetic algorithm is used to optimize the connection weight between the input layer and the hidden layer and threshold of the extreme learning machine, which contributes to increase prediction performance. The test result shows that the optimized model can effectively imitate the dynamic trends of actual measured data with appropriate accuracy, the mean absolute percentage error of SO2 concentration and NOx concentration are 4.63% and 3.09% respectively. And the model's satisfactory generalization ability is demonstrated based on the generalization experiment. In addition, compared with other methods, the proposed modeling method for SO2-NOx concentration has more suitability and accuracy under dynamic conditions, which contributes to online optimization of pollutant control and intelligent development of circulating fluidized bed units.

Quantum Inspired Genetic Algorithm Model based Automatic Modulation Classification

The popularity of automatic modulation categorization (AMC) is high in recent years owing to the many advantages. When it comes to communication, reliability in an AMC is very critical. Increasing the amount of signals exponentially increases the cost of using the AMC. Precise classification methods, such as neural network, in which either the parameters of the neural network or the dimensions of the input or output variables are modified dynamically, are not successful in obtaining high accuracy results. To improve the accuracy of the modulation categorization, this study employs a "QIGA" feature selection model based on Quantum (inspired) Genetic Algorithm (QIGA). QIGA is used to choose the correct functionality, and to limit the number of examples that must be learned so that the overall system time is shortened and the cost of computing is reduced. Selecting excellent characteristics is enhanced via quantum computing, and this is done to lower the complexity of the solutions. The internal validation results demonstrated that the QIGA model significantly improved the statistical match quality and significantly outperformed the other models.

Quantum Algorithm of Imperfect KB Self-organization Pt I: Smart Control-Information-Thermodynamic Bounds

The quantum self-organization algorithm model of wise knowledge base design for intelligent fuzzy controllers with required robust level considered. Background of the model is a new model of quantum inference based on quantum genetic algorithm. Quantum genetic algorithm applied on line for the quantum correlation’s type searching between unknown solutions in quantum superposition of imperfect knowledge bases of intelligent controllers designed on soft computing. Disturbance conditions of analytical information-thermodynamic trade-off interrelations between main control quality measures (as new design laws) discussed in Part I. The smart control design with guaranteed achievement of these tradeoff interrelations is main goal for quantum self-organization algorithm of imperfect KB. Sophisticated synergetic quantum information effect in Part I (autonomous robot in unpredicted control situations) and II (swarm robots with imperfect KB exchanging between “master - slaves”) introduced: a new robust smart controller on line designed from responses on unpredicted control situations of any imperfect KB applying quantum hidden information extracted from quantum correlation. Within the toolkit of classical intelligent control, the achievement of the similar synergetic information effect is impossible. Benchmarks of intelligent cognitive robotic control applications considered.

A novel IMC-FOF design for four wheel steering systems of distributed drive electric vehicles

To improve handling and stability for distributed drive electric vehicles (DDEV), the study on four wheel steering (4WS) systems can improve the vehicle driving performance through enhancing the tracking capability to desired vehicle state. Most previous controllers are either a large amount of calculation, or requires a lot of experimental data, these are relatively time-consuming and laborious. According to the front and rear wheel steering angle of DDEV can be distributed independently, a novel controller named internal model controller with fractional-order filter (IMC-FOF) for 4WS systems is proposed and studied in this paper. The IMC-FOF is designed using the internal model control theory and compared with IMC and PID controller. The influence of time constant and fractional-order parameters which is optimized using quantum genetic algorithms (QGA) on tracking ability of vehicle state are also analyzed. Using a production vehicle as an example, the simulation is performed combining Matlab/Simulink and CarSim. The comparison results indicated that the proposed controller presents performance to distribute the front and rear wheel steering angle for ensuring better tracking capability to desired vehicle state, meanwhile it possesses strong robustness.

The Rheological Analytical Solution and Parameter Inversion of Soft Soil Foundation

In soft soil engineering projects, the building loads are always required to be symmetrically distributed on the surface of the foundation to prevent uneven settlement. Even if the buildings and soft clay are controlled by engineers, it can still lead to the rheology of the foundation. The analytical solution based on the Laplace integral transformation method has positive significance for providing a simple and highly efficient way to solve engineering problems, especially in the long-term uneven settlement deformation prediction of buildings on soft soil foundations. This paper proposes an analytical solution to analyze the deformation of soft soil foundations. The methodology is based on calculus theory, Laplace integral transformation, and viscoelastic theory. It combines an analytical solution with finite theory to solve the construction sequences and loading processes. In addition, an improved quantum genetic algorithm is put forward to inverse the parameters of soft soil foundations. The analytical solution based on Laplace integral transformation is validated through an engineering case. The results clearly illustrate the accuracy of the method.

Developing a Resource Allocation Approach for Resource-Constrained Construction Operation under Multi-Objective Operation

In the construction industry, it is of great importance for project managers (PM) to consider the resource allocation arrangement problem based on different perspectives. In this situation, the management of resources in construction becomes a challenge. Generally speaking, there are many objectives that need to be optimized in construction that are in conflict with each other, including time, cost, and energy consumption (EC). This paper proposed a multi-objective optimization framework based on the quantum genetic algorithm (QGA) to obtain the best trade-off relationship among these goals. The construction resources allocated in each construction activity would eventually determine its execution time, cost, and EC, and a complexed time-cost-energy consumption trade-off framework of the project is finally generated due to correlations between construction activities. QGA was performed to find the best combination among time, cost, and EC and the optimal scheme of resource arrangement under this state. The construction process is simulated in BIM to check the rationality of this resource allocation mode. An industrial plant office building in China is presented as an example to illustrate the implementation of the proposed model. The results show that the presented method could effectively reduce 7% of cost, 17% of time, and 21% of energy consumption. This developed model is expected to help PMs to solve the problem of multi-objective optimization with limited resource allocation.

An Effective Quantum Genetic Algorithm Based on Drama Resource Mining Using Wireless Sensing Technology

Wireless sensor technology has penetrated various domains of today’s life and plays a vital role in the advanced technology. Numerous researchers have combined this outstanding technology with other fields such as resource mining, industry, healthcare, automobile system, gaming industry, and dramas. However, in traditional resource mining, long mining time leads to incomplete mining results along with low accuracy. In order to improve the effect of resource mining, we have proposed an effective Quantum Generic Algorithm (QGA) based on drama resource mining by using wireless sensor technology. In our proposed scheme, we have combined the RFID technology of wireless sensor with wireless network protocol stack for the purpose of collecting drama resources on the network platform. We have classified the drama resources on the network platform by using QGA based on the results of resources collection. Additionally, we have mined the semantic association features of frequent patterns of the drama resources on the platform and combined with the fuzzy attribute feature detection method. The experimental results show that this method is superior to the traditional methods in terms of resource mining time, mining results’ comprehensiveness, and mining results’ accuracy, which shows that this method has practical application value.

Implementing evolutionary optimization on actual quantum processors

This paper introduces a new evolutionary algorithm with the support of an actual quantum processor, a computing device which uses phenomena from quantum mechanics to enable a considerable speed-up in computation. In particular, the proposed approach uses quantum superposition and entanglement to implement quantum evolutionary concepts such as quantum chromosome, entangled crossover, rotation mutation, and quantum elitism, to efficiently perform genetic evolution on quantum devices, and converge towards proper sub-optimal solutions of a given optimization problem. The proposed quantum genetic algorithm has been implemented by using a hybrid hardware architecture, where classical processors interact with the family of quantum processors provided by the IBM Q Experience® initiative. As shown in the experimental section, the proposed quantum genetic algorithm’s performance highlights that the synergy between quantum and evolutionary computation results in a new and promising bio-inspired optimization strategy.

Delay optimization for ternary fixed polarity Reed–Muller circuits based on multilevel adaptive quantum genetic algorithm

Delay optimization has now emerged as an important optimization goal in logic synthesis. The delay optimization for ternary fixed polarity Reed–Muller (FPRM) circuits aims to find a ternary FPRM circuit with a minimum delay. Because the delay optimization for ternary FPRM circuits is a combinatorial optimization problem, in this paper, we first propose a multilevel adaptive quantum genetic algorithm (MAQGA), which divides individuals into three-level populations: high-level population, intermediate-level population, and low-level population and uses the proposed ternary quantum rotation gate, proposed ternary quantum correction gate, and proposed multi-operator adaptive mutation mechanism to make the three-level populations evolve. Moreover, based on the proposed delay decomposition strategy, we propose a delay optimization approach (DOA) for ternary FPRM circuits under the unit delay model, which searches for a ternary FPRM circuit with a minimum delay using the MAQGA. Experimental results demonstrated the effectiveness and superiority of the DOA in optimizing the delay of ternary FPRM circuits.