Quantum Genetic Algorithm Research Articles

Quantum genetic sliding mode controller design for depth control of an underwater vehicle

Quantum genetic algorithm (QGA) is an optimization algorithm based on the probability that combines the idea of quantum computing and traditional genetic algorithm. In this paper, a new type of control law is developed for an underwater vehicle along with the desired path. The proposed controller is based on sliding mode control (SMC) in which the reaching law is modified to overcome two challenging problems, chattering, and sensitivity against noise. The disturbance is considered as a set of sinus waves with different frequencies which its parameters are estimated by Particle Swarm Optimization (PSO). Since QGA has some advantages such as fast convergence speed, small population size, and strong global search capabilities, we use QGA to determine the gain of the proposed controller. Finally, the Lyapunov theorem is used to prove that trajectory-tracking error converges to zero. Simulation results show that QGA can converge to the optimal response with a population consist of one chromosome.

Adaptive quantum genetic algorithm for task sequence planning of complex assembly systems

Task sequence planning (TSP) is the key factor to the efficiency, stableness, and cost of a complex assembly system. To address the issue, an adaptive quantum genetic algorithm based on artificial potential field and gradient of object function is proposed to optimise the solving process, and to obtain the optimal TSP scheme. The simulation results indicate that the proposed algorithm can perform higher efficiency and stableness than the previously reported methods.

Quantum Genetic Algorithm on Multilevel Quantum Systems

We present a new evolutionary algorithm on the basis of quantum computations technology for solving optimization problems. The algorithm is built using many-valued quantum logic concept, which is more prospective from the computing power’s point of view. We compare the suggested algorithm to the traditional quantum genetic algorithm to demonstrate its high effectiveness on the example of test function global optimization problems. The advantages can be observed in the running time, the convergence speed, and the solution precision. The proposed implementation for the algorithm of quantum gate operator has an adaptive nature and does not require a lookup table. The role and the influence mechanism of the quantum disaster operator on the proposed algorithm effectiveness are also analyzed.

An adaptive quantum evolution algorithm for 0–1 knapsack problem

Quantum Genetic Algorithm (QGA) has a number of advantages in comparison with its classical version: operating speed, small population size and auto-balance between the global search and the local search. It is based on the ideas of traditional evolutionary algorithms, applied to the quantum computations technology, which operate with quantum bits, superposition of states and quantum measurements. This paper proposes a QGA with a new adaptive quantum gate operator and a restoring technology for the quantum chromosome during the process of solving combinatorial problems with constraints. Meta-optimization of the primary algorithm parameters is used for providing the algorithm efficiency. The productiveness of the suggested approach is proven by the model studies, carried out using a wide range of test 0–1 knapsack problems.

IQGA: A route selection method based on quantum genetic algorithm- toward urban traffic management under big data environment

The increasingly serious problem of traffic congestion has become a critical issue that urban managers need to focus on. However, as urban scale and structure have already taken shape, the use of existing road resources to achieve effective route selection for vehicles is the key to solving this traffic congestion problem. Existing research has mainly focused on the following three points: (1) algorithms for controlling traffic signal lamp period at single intersections; (2) route recommendation algorithms for a single vehicle; and (3) route recommendation algorithms based on the traffic history experienced by a vehicle. These studies, however, have the following limitations: (1) the evaluation factor is singular, and therefore, cannot fully express the advantages and disadvantages of the route selection method; (2) real-time route selection is absent; (3) route selection for a single vehicle is ineffective in avoiding local congestion. In view of these problems, this paper proposes an improved quantum genetic algorithm (IQGA) to solve the problem of traffic congestion in route selection. The algorithm includes the following: (1) proposing a quantum chromosome initialization strategy (QCIS) to convert and code real traffic conditions and to construct quantum chromosomes based on the quantum coding for vehicles and roads; (2) proposing a quantum chromosome mapping algorithm (QCMA) to transform the calculation bits of quantum chromosomes into the results of route selection for different vehicles; (3) proposing a contemporary optimal solution decision strategy (COSDS) to judge the current route selection results; (4) proposing a quantum update algorithm (QUA) to update and iterate the quantum coding of the population. Two types of experiments were conducted in this study: (1) Artificial traffic networks with different scales were designed to carry out comparative experiments between IQGA and other algorithms. The experimental results show that IGQA has better robustness and adaptive ability. (2) Comparative experiments on an actual urban traffic network verified the high-performance and real-time performance capabilities of IQGA.

Cooperative Coalition Selection for Quality of Service Optimization in Cluster-Based Capillary Networks

Cooperative multiple-input-single-output (CMISO) scheme has been proposed in the literature to prolong the network lifetime in the cluster-based Internet of Things (IoT) systems. CMISO scheme increases the spatial diversity of wireless channels, however, it reduces the transmit power and thus degrades the quality of service (QoS) performance. In this paper, we formulate the problem of cooperative coalition selection for CMISO scheme to minimize the overall packet error rate (PER). Then, we propose to apply the qubit-based quantum-inspired particle swarm optimization (QPSO) to select the optimum cluster head (CH) and cooperative devices coalition. Simulation results proved that the qubit-based QPSO has faster convergence speed and outperforms $\Psi$ -based QPSO, particle swarm optimization (PSO), and quantum genetic algorithm (QGA) in terms of overall PER.

In-flight compound homing methodology of parafoil delivery systems under multiple constraints

In order to realize accurate and secure homing control of the parafoil delivery systems under multiple constraints, an in-flight compound homing methodology is designed in this paper. It consists of the modeling of parafoil delivery systems, a trajectory optimization method based on quantum genetic algorithm, a control approach based on active disturbance rejection control. Firstly, the precise model of parafoil delivery system is established by the actual flight data. Then, in order to restrain the wind disturbance, a novel wind identification method is introduced into the trajectory optimization method. Combining the wind identification method and terrain-matching, the proposed trajectory optimization method highlights its improvement from the traditional homing approach. It can not only obtain an optimal trajectory in windy environment, but also maintain the essential advantage of the multiphase homing methodology – great realizability. Then, the ADRC controller is designed. It is applied to resist the varying wind and other disturbance. At last, the hardware-in-the-loop simulation results show that the proposed compound homing methodology can achieve excellent effects both in the trajectory optimization and trajectory tracking. In this paper, the proposed methodology is also compared with the trajectory optimization based on chaos particle swarm optimization method and the PID technology of the trajectory tracking. The results also present huge improvement and wide application prospect.

Mediterranean Diet Optimization by Improved Hybrid Quantum Genetic Algorithm

Mediterranean Diet Optimization by Improved Hybrid Quantum Genetic Algorithm

A parallel adaptive quantum genetic algorithm for the controllability of arbitrary networks.

In this paper, we propose a novel algorithm—parallel adaptive quantum genetic algorithm—which can rapidly determine the minimum control nodes of arbitrary networks with both control nodes and state nodes. The corresponding network can be fully controlled with the obtained control scheme. We transformed the network controllability issue into a combinational optimization problem based on the Popov-Belevitch-Hautus rank condition. A set of canonical networks and a list of real-world networks were experimented. Comparison results demonstrated that the algorithm was more ideal to optimize the controllability of networks, especially those larger-size networks. We demonstrated subsequently that there were links between the optimal control nodes and some network statistical characteristics. The proposed algorithm provides an effective approach to improve the controllability optimization of large networks or even extra-large networks with hundreds of thousands nodes.

Parameter optimization of plug-in hybrid electric vehicle based on quantum genetic algorithm

The parameters of the power system and the control strategy parameters are coupled,they work together to affect the power, economy and emission of the plug-in hybrid electric vehicle. Firstly, the real-time optimal control strategy based on the optimum of the vehicle power system’s efficiency is developed. Secondly, the cost of the power system and indicators of vehicle’s power performance are taken as constraints, the fuel consumption and emissions considering the engine’s cold-heat effect are taken as the optimization objective, and in order to obtain the optimum solution of the power system’s parameters and control strategy parameters, the standard genetic algorithm (SGA) and the quantum genetic algorithm (QGA) are applied to coordinated optimize them respectively. Finally, the simulation model of the vehicle system is established. After simulation, the simulation results show that compared with the original vehicle, the fuel economy and emission performance of the vehicle which parameters optimized by SGA and QGA has greatly improved, compared to the parameters optimization by SGA, the optimization by QGA can suppress premature convergence and have better fuel economy and emission performance.

Modelling the spice parameters of SOI MOSFET using a combinational algorithm

Progress in the technology of submicron semiconductor device, which makes a short-channel and quantum effects, having equations of nonlinear modelling, leads to complicated and time-consuming calculations. In order to control these complexities and obtain the device characteristics according to device parameters, a faster method is needed. In this paper, a combinational algorithm is proposed for modelling a nano silicon-on-insulator metal–oxide–semiconductor field effect transistor (SOI MOSFET) characteristic. The proposed method shows the same device characteristics with lower input parameters. In this method, a combination of genetic algorithm (GA) and artificial neural network are used. Then quantum evolutionary algorithm (QEA) is employed instead of genetic algorithm (GA) for comparing and modifying algorithm. Results show that the algorithm’s accuracy is 95% and 98% for test data of GA and QGA, respectively. Moreover, the reduction percentage of input parameters are 11% and 52% for GA and QEA, respectively. The simulation results represent that the implemented quantum genetic algorithm for prediction of device characteristics is more effective and accurate than GA.

Identification of Flux Linkage Map of Permanent Magnet Synchronous Machines Under Uncertain Circuit Resistance and Inverter Nonlinearity

This paper proposes a novel scheme for the identification of the whole flux linkage map of permanent magnet synchronous machines, by which the map of dq -axis flux linkages at different load or saturation conditions can be identified by the minimization of a proposed estimation model. The proposed method works on a conventional three-phase inverter based vector control system and the immune clonal based quantum genetic algorithm is employed for the global searching of minimal point. Besides, it is also noteworthy that the influence of uncertain inverter nonlinearity and circuit resistance are cancelled during the modeling process. The proposed method is subsequently tested on two PMSMs and shows quite good performance compared with the finite element prediction results.

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.

Quantum Genetic Algorithm Based on Qutrits and Its Application

Typical approaches to designing quantum genetic algorithms are based on a concept of a qubit, a two-level quantum system. But many-valued quantum logic is more perspective from the point of view of the computational power. This paper proposes a quantum genetic algorithm based on a three-level quantum system in order to accelerate evolutionary process. Simulation using a set of standard test functions proves that the given algorithm is more effective and precise than the conventional quantum genetic algorithm.

Revenue Optimization of Pipelines Construction and Operation Management Based on Quantum Genetic Algorithm and Simulated Annealing Algorithm

For the optimization of pipelines, most researchers are mainly concerned with designing the most reasonable section to meet the requirements of strength and stiffness, and at the same time reduce the cost as much as possible. It is undeniable that they do achieve this goal by using the lowest cost in design phase to achieve maximum benefits. However, for pipelines, the cost and incomes of operation management are far greater than those in design phase. Therefore, the novelty of this paper is to propose an optimization model that considers the costs and incomes of the construction and operation phases, and combines them into one model. By comparing three optimization algorithms (genetic algorithm, quantum genetic algorithm and simulated annealing algorithm), the same optimization problem is solved. Then the most suitable algorithm is selected and the optimal solution is obtained, which provides reference for construction and operation management during the whole life cycle of pipelines.

Fault Diagnosis of Rotation Machinery Based on Support Vector Machine Optimized by Quantum Genetic Algorithm

Considering the disadvantages of conventional fault diagnosis methods for rotating machinery, such as low efficiency and low accuracy, we propose a fault diagnosis method based on support vector machine (SVM) optimized by quantum genetic algorithm (QGA). First, the SVM parameters are optimized by QGA. Then, the training data set is used to train the SVM model, and the test data set is used for model testing. The experimental results show that the proposed method has higher accuracy in fault diagnosis than traditional SVM method and the method based on genetic algorithms and SVM.

A novel cognitive radio spectrum allocation scheme with chaotic gravitational search algorithm

Cognitive radio (CR) is a promising solution to the lack of spectrum, as well as spectrum inefficiency, in current communication networks. A major demand of this technology is for efficient and fair spectrum assignment to unlicensed (secondary) users. This paper presents a novel method to address this challenge in CR networks, based on the graph colouring problem (GCP) and gravitational search algorithm (GSA). In addition, the chaotic search process has been designed in the method to avoid falling into local optimal solution. Last, we compare the performance of the proposed algorithm with particle swarm optimisation (PSO), genetic algorithms (GA) and quantum genetic algorithm (QGA).

A novel cognitive radio spectrum allocation scheme with chaotic gravitational search algorithm

Cognitive radio (CR) is a promising solution to the lack of spectrum, as well as spectrum inefficiency, in current communication networks. A major demand of this technology is for efficient and fair spectrum assignment to unlicensed (secondary) users. This paper presents a novel method to address this challenge in CR networks, based on the graph colouring problem (GCP) and gravitational search algorithm (GSA). In addition, the chaotic search process has been designed in the method to avoid falling into local optimal solution. Last, we compare the performance of the proposed algorithm with particle swarm optimisation (PSO), genetic algorithms (GA) and quantum genetic algorithm (QGA).

FSQGA based 3D complexity wellbore trajectory optimization

Determination of the trajectory of a complex wellbore is very challenging due to the variety of possible well types, as well as the numerous complicated drilling variables and constraints. The well type could be directional wells, cluster wells, horizontal wells, extended reach wells, redrilling wells, and complex structure wells, etc. The drilling variables and constraints include wellbore length, inclination hold angles, azimuth angles, dogleg severity, true vertical depths, lateral length, casing setting depths, and true vertical depth. In this paper, we propose and develop an improved computational model based on Fibonacci sequence to adjust the quantum rotation step in quantum genetic algorithm for achieving cost-efficient complex wellbore trajectories. By using Fibonacci sequence based quantum genetic algorithm (FSQGA) in a complex searching problem, we can find high-quality globally optimal solutions with high speed through a parallel process. The simulation results show that FSQGA can significantly reduce computation complexity, and reach minimum objection values faster. Meanwhile, minimization of the true measurement depth of complex wellbore trajectory in actual gas-oil field shows that the drilling cost can be reduced up to 4.65%. We believe this new algorithm has the potential to improve drilling efficiency, to reduce the drilling time and drilling cost in real-time wellbore trajectory control.

Quantum Genetic Algorithm for Evolving Neural Controllers

Quantum Genetic Algorithm for Evolving Neural Controllers