Quantum Genetic Optimization Research Articles

A novel digital extraction approach of pore network models from carbonates inspired by quantum genetic optimization techniques

A novel digital extraction approach of pore network models from carbonates inspired by quantum genetic optimization techniques

A Setting Optimization Ensemble for a Distributed Power Grid Protective Relay

To ensure a stable and reliable power supply, the valid and timely response of protective relays are indispensable. Through the prevention of fault expansions, potential equipment damage or system collapse can be averted, where their setting is one vital prerequisite for such effective implementations. However, the increasing complexity of distribution power systems results in more challenges for protection tuning strategies. Ergo, this paper presents an ensemble that combines the independent factor evaluation (IFE) and quantum genetic optimization (QGO) models to further optimize the performance of relays according to their distributed tuning environment. In this ensemble, both near and far-end fault characteristics can be incorporated. In the first stage, the IFE dimensional reduction model is deployed for massive heterogeneous input data, where the statistical independence of input signals is calculated, the linear transformation matrix to decouple mixed signals is found, the linear combination of such signals is formed, and the non-Gaussian property to sort them is established. This can ameliorate the following calculation efficiency under those high-dimensional data scenarios. Subsequently, the QGO model is designed to further improve relay settings, where qubit representation is built to reduce required chromosomes, the linear superposition of the optimal solution probability in different states is implemented for a better diversity and convergence performance, and a self-adaption quantum gate is established to dynamically update the qubit chromosome groups and two-state solution combinations. Lastly, an empirical case study is presented, which validates the enhanced convergence, accuracy, and rapidity of the proposed ensemble.

A New Fog-Based Transmission Scheduler on the Internet of Multimedia Things Using a Fuzzy-Based Quantum Genetic Algorithm

The Internet of Multimedia Things (IoMT) has recently experienced a considerable surge in multimedia-based services. Due to the fast proliferation and transfer of massive data, the IoMT has service quality challenges. This paper proposes a novel fog-based multimedia transmission scheme for IoMT using the Sugano interference system with a quantum genetic optimization algorithm. The fuzzy system devises a mathematically organized strategy for generating fuzzy rules from input and output variables. The Quantum Genetic Algorithm (QGA) is a metaheuristic algorithm that combines genetic algorithms and quantum computing theory. It combines many critical elements of quantum computing, such as quantum superposition and entanglement. This provides a robust representation of population diversity and the capacity to achieve rapid convergence and high accuracy. As a result of the simulations and computational analysis, the proposed fuzzy-based QGA scheme improves packet delivery ratio and throughput by reducing end-to-end latency and delay when compared to traditional algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Heterogeneous Earliest-Finish-Time (HEFT) and Ant Colony Optimization (ACO). Consequently, it provides a more efficient scheme for multimedia transmission in IoMT.

Improving quantum genetic optimization through granular computing

Quantum computers promise to revolutionize the world of computing thanks to some features of quantum mechanics that can enable massive parallelism in computation. This benefit may be particularly relevant in the design of evolutionary algorithms, where the quantum paradigm could support the exploration of multiple regions of the search space in a concurrent way. Although some efforts in this research field are ongoing, the potential of quantum computing is not yet fully expressed due to the limited number of qubits of current quantum processors. This limitation is even more acute when one wants to deal with continuous optimization problems, where the search space is potentially infinite. The goal of this paper is to address this limitation by introducing a hybrid and granular approach to quantum algorithm design, specifically designed for genetic optimization. This approach is defined as hybrid, because it uses a digital computer to evaluate fitness functions, and a quantum processor to evolve the genetic population; moreover, it uses granular computing to hierarchically reduce the size of the search space of a problem, so that good near-optimal solutions can be identified even on small quantum computers. As shown in the experiments, where IBM Q family processors are used, the usage of a granular computation scheme statistically enhances the performance of the state-of-the-art evolutionary algorithm implemented on quantum computers, when it is run to optimize well-known benchmark continuous functions.

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.

Application of Quantum Genetic Optimization of LVQ Neural Network in Smart City Traffic Network Prediction

Accurate prediction of traffic flow in urban networks is of great significance for smart city management. A short-term traffic flow prediction algorithm of Quantum Genetic Algorithm - Learning Vector Quantization (QGA-LVQ) neural network is proposed to forecast the changes of traffic flow. Different from BP neural network, Learning Vector Quantization (LVQ) neural network is of simple structure, easy implementation and better clustering effect. Utilizing the global optimization ability of Quantum Genetic Algorithm (QGA), it is combined with LVQ neural network to overcome some shortcomings of LVQ neural network, including sensitive to initial weights and prone to local minima. In order to test the convergence ability and the timeliness of QGA-LVQ neural network in short-term traffic flow, some contrast experiments are performed. Experimental simulation results show that, QGA-LVQ neural network obtains excellent prediction results in prediction accuracy and convergence speed. Besides, compared with GA-BP neural network and wavelet neural network, QGA-LVQ neural network performs better in short-term traffic flow prediction.

An improved SVM model with double quantum genetic optimization and its application in fault diagnosis of rolling bearing

An improved SVM model with double quantum genetic optimization and its application in fault diagnosis of rolling bearing

A Fuzzy C-Means Clustering Algorithm Based on Improved Quantum Genetic Algorith

Aiming at the problem of traditional fuzzy C-means clustering algorithm that it is sensitive to the initial clustering centers and easy to fall into the local optimization, an improved algorithm that combines Improved Quantum Genetic Optimization with FCM algorithm is proposed. In this study, chromosomes are comprised of quantum bits encoded by real number. Chromosomes are renovated by quantum rotating gates and mutated by quantum hadamard gate. The gradients of object function are utilized in adjusting the value of rotating angle by a dynamic strategy. Each chain of genes represents a optimization result, Therefore, a double searching space is acquired for the same number of chromosomes. Experimental results show that the proposed method improves the stability and the accuracy of classification．

Quantum Genetic Optimization of Spindle Speed Ratio in Numerical Control Lathe with Automatic Transmission

Determination of spindle rotate speed of lathe is an important issue when machine tools with changeable spindle gear ratio cutting metal parts, especially in heavy load cutting. Genetic optimization is used to automate design in engineering application for minimizing volume or weight. A quantum genetic optimization method was applied to calculate rotate speed and ratio of spindle transmission for gain maximum motor efficiency in numerical control lathe. Quantum bit code way was applied to simplify calculation and improve compatibility. Rotate angle strategy gate were introduced to Quantum gate for realizing evolution operation. The results were compared with a trial and error procedure usually applied. Quantum genetic arithmetic produced quite well results promptly supplying economic cutting parameters of numerical control (NC) lathe.

Immune-Inspired Quantum Genetic Optimization Algorithm and its Application

Artificial immune systems (AIS), inspired by the natural immune systems, are an emerging kind of soft computing methods. This paper brings forward an immune-inspired quantum genetic optimization algorithm (IQGOA) based on clonal selection algorithm. The IQGOA is an evolutionary computation method inspired by the immune clonal principle of human immune system. To show the versatility and flexibility of the proposed IQGOA, some examples are given. Experimental results have shown that IQGOA is superior to clonal selection algorithm and Genetic Algorithm (GA) on performance.

Quantum Genetic Optimization

The complexity of the selection procedure of a genetic algorithm that requires reordering, if we restrict the class of the possible fitness functions to varying fitness functions, is , where is the size of the population. The quantum genetic optimization algorithm (QGOA) exploits the power of quantum computation in order to speed up genetic procedures. In QGOA, the classical fitness evaluation and selection procedures are replaced by a single quantum procedure. While the quantum and classical genetic algorithms use the same number of generations, the QGOA requires fewer operations to identify the high-fitness subpopulation at each generation. We show that the complexity of our QGOA is in terms of number of oracle calls in the selection procedure. Such theoretical results are confirmed by the simulations of the algorithm.