Earticle

Genetic algorithm was used to identify the damage of frame structure. Stiffness coefficient damage factor is selected as design variable, and the weighted array difference value between inherent frequency and vibration mode of structure calculated and measured. According to the difficulty in selecting crossover rate and mutational rate for fundamental algorithm, the process of selection operator, crossover operator and mutation operator was improved. All operators were operated on parent individual. Crossover rate and mutational rate were set for 100%. Punishment function was applied for keeping the difference among individuals. The improved genetic algorithm can conserve the better individual in parent and keep off fall into local optimum. Through a 3-story frame with single variable damage and multiple variables damage study, the results showed that the improved genetic algorithm can identify the damage location and degree.

In this paper, in order to assess the risk of network, network security assessment process being involved in the content in detail. The above-mentioned research-based support system platform security test and evaluate research of the safety situation assessment. Prediction subsystem detailed design and carry out the implementation. In this paper, network security issues, as a detailed study of neural networks knowledge. Focus on the evaluation methods and calculation rules of nerve network technology, it has been studied by specific examples. Calculation demonstrated the feasibility of neural network evaluation model through actual case, which pointed out the traditional methods. This paper focuses on the network security assessment based on neural network technology, extensive analysis of the proposed major modeling tool indicator system for network security analysis. The application of neural networks was a network security assessment and to optimize the network by genetic algorithm. The key parameter combination operated efficiency of neural networks to get better play.

Threshold is presented according to the principle of genetic algorithm, the secondary search optimization of the improved genetic algorithm is divided into two times to seek the global optimal solution, namely using the results of the search for solution for the first time to determine the second optimization selection of initial population, as a result of the optimization may be given for the first time is not necessarily the global optimal solution, but it certainly is a good result, the optimization process will be the second time, the initial population of restrictions in a neighborhood of the search results for the first time. The simulation results proved the advantage of the proposed algorithm.

To solve a state transfer matrix using a Markov model, an improved genetic algorithm approach is proposed. A Markov prediction model is employed to study structures in the SME technical efficiency and scale gains calculation method. The obtained results are compared to the data shown in the China Statistical Yearbooks. It is found that our proposed genetic algorithm approach provides references for optimizing an agriculture SME industrial structure and hence improves the prediction precision.

This paper proposes the community detection in complex networks based on improved genetic algorithm and local optimization (IGALO) in terms of the defect that traditional community detection approaches based on genetic algorithm have strong randomness and weak searching ability in the process of community detection. Taking modularity function Q as the objective function, IGALO algorithm adopts label propagation method of one-iteration to initialize population so as to generate initial population with certain precision. Then, anti-destructive one-way crossover strategy is proposed to ensure the crossover operation to develop in the direction of making community structure increase to modularity function. Finally, mutation strategy of node local optimization is proposed to improve the searching efficiency of algorithm. This algorithm effectively overcomes the defect that traditional algorithms have weak searching ability and improves the community detection accuracy. Tests are made on benchmark networks and real-world networks and comparative analysis is also made with various classic algorithms. The results show that IGALO algorithm is effective and feasible.

The traditional genetic algorithm randomly selects nodes in two chromosomes for crossover operation, which may result in individuals of disconnected or loop circuit and lead to issues as meaningless crossover operations. In order to increase the diversity of the population and prevent the occurrence of premature mutation algorithm which might cause local convergence, this essay presents a new urban traffic optimal path planning method. Initialized from the improvement of population genetic algorithm, it designs the fitness function and optimizes crossover and mutation operators so that the optimal or near-optimal solution can be quickly figured out. Moreover, the Matlab software simulation test exhibits the feasibility and effectiveness of the method.

The system of distribution network with distributed generation is analyzed, and dynamic optimization based on an improved genetic algorithm is presented in this paper. First, the mathematical model of distribution network can be established by using constraints and objective function, which contains the network loss, the DGs investment and reliability of power supply. Then, according the construction of distribution network, this paper presents a design of improved genetic algorithm. Finally, IEEE 14 nodes system is adopted to realize the design of the algorithm.

Optimal solution and simulation has been made for model through improved genetic algorithm based on the established mathematical model. Distribution route optimization problem can be solved by algorithm in a better way based on the features of distribution optimization problem of complicated path and through improved genetic algorithm and adding population pretreatment operator; finally, simulation experiment has been made through MATLAB simulation software.

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．

The paper provides an improved evolutionary strategy (ES) of genetic algorithm (GA) on the basis of the existing literature. The ES overcomes the shortage of traditional GA whose excellent child individuals obtained in the crossover process may not survive in the process of mutation. In addition, the crossover probability and mutation probability which is hard to determine in traditional GA is removed for this proposed strategy. At the same time, it increases the number of individuals produced in process of crossover. This may increase the possibility of producing excellent individuals, thus lead to better improvement of the traditional GA. The test result of finding the optimal values of four functions using transitional GA and the proposed GA is presented in this paper. The result shows that the improved ES presented in this paper has faster calculation speed and significantly smaller number of iterations than the traditional GA. Thus, the improvement of improved ES is powerfully illustrated. Based on articles in the existing research literature, the initial population generation methods were further explored when using the genetic algorithm(GA) for solving constrained optimization problem. Through the research we present a new method about initial interior point’s generation. Firstly, construct a constraint posed by the objective function, which is based on the characteristics of constrained optimization problems. Then translate the problem of evaluating the initial interior point into a problem of solving a series of unconstrained optimization. By solving the unconstrained optimization problem, we achieve the solution of the initial interior point. Based on this idea, the research has given a method on the generation of the rest initial population individuals. In addition, through the research we concluded that the key to generate the initial population is to obtain an initial point. The production of other individuals will take less time after the initial internal point is obtained. Finally, we verified by examples that the initial population generation method given by this paper is a fast and reliable method. Thus the shortage of the GA of which the initial population is difficult to be produced in some constrained optimization problem is overcome

Based on the analysis on the basic principles and characteristics of the existing multi-objective genetic algorithm (MOGA), an improved multi-objective GA with elites maintain is put forward based on non-dominated sorting genetic algorithm (NSGA). NSGA-II algorithm theory and parallel hybrid evolutionary theory is described in detail. The design principle, process and detailed implementations of the improved MOGA are given. IMNSGA-II algorithm and NSGA-II algorithm are applied to test the performance of the two algorithms for different test function, experiments of example are preformed. Experimental results show that the improved MOGA achieved the optimal between the convergence and diversity.

Boiler efficiency and emission load of NOX are the key evaluation indicators of operation performance of the coal-fired boiler. It has become a popular academic research topic concerning the reduction of NOX emissions while maintaining the same boiler efficiency, as well as how to build a model for boiler emission. Based on a prediction model that is constructed for boiler efficiency and emission load of NOX with the application of deep belief algorithm, genetic algorithm is used to optimize the tilting angel of boiler burners and the flow velocity of pulverized coal, thereby effectively reducing the emission load of NOX. Simulation results indicate that this method effectively optimizes the parameters of the boiler, and provides a new way to optimize the parameters of the boiler.

Genetic algorithms for multi-objective optimization problem to be solved were studied. Through the elitist strategy analysis, it is an improved multi-objective optimization algorithm. The algorithm uses a data warehouse to store the optimal solution produced by individuals in each generation, from the way individuals adopt measures to phase out the individual data warehouse identical or similar, the algorithm also improved selection operator, so that the algorithm adaptive capacity enhancement, the new algorithm improves the algorithm performance, improves the quality of understanding between sets, can get a lot of optimal and balanced.

Aiming at the degeneration phenomenon of images taken in mist, according to the features of the degraded images, an improved haze removal algorithm based on genetic fuzzy clustering is presented in this paper after analyzing its defects and shortcomings. Firstly, the improved atmospheric scattering model is established. Secondly, the original image is converted into a standard image through the improved model, and then we present a new multi-scale image edge detection by genetic fuzzy clustering, Based on this observation, we can use the multi-scale image edge detection to estimate the haze thickness directly and recover a high quality haze-free image. The new algorithm uses good global search ability of the genetic algorithm, which will implement the transfer from the scene defogging problem into the optimal estimation problem under global contrast optimal point. Compared with other algorithms for degraded images, the improved haze removal algorithm not only detects image edge precisely, but also has better performance in situations of dense haze. Theoretical analysis and experimental results demonstrate that, the new algorithm improved in this paper are effective for removal of fog-degraded images, and can be applied to the practical situations.

The combination of traditional ant colony algorithm in solving the optimization process to consume a large amount of time, easily falling into local optimal solution and convergence is slow and other disadvantages, while also generating a lot of useless redundant iterative code, operation efficiency is low. Therefore, ant colony optimization algorithm is proposed. The algorithm based on genetic algorithm has the ability to search the global ant colony algorithm also has a parallel and positive feedback mechanisms. Changes in the use of genetic algorithm selection operator, crossover operator and mutation operator action to determine the distribution of pheromone on the path, the ant colony algorithm for feature selection using support vector machine classifiers for evaluating the performance characteristics of the feedback sub-Variorum And by changing the pheromone iteration, parameter selection and increase the local pheromone update feature nodes guided the re-combination. The algorithm uses probability expectation values are obtained to meet under the conditions with minimal sensor nodes, and gives the optimal coverage and connectivity probability models and reasoning. The experimental results show that, the algorithm can not only use the least nodes complete the effective target area to be covered, and in reducing the network energy consumption is also greatly improved, simultaneously reduces the cyber source configuration, improve the network life cycle.

Aiming at the existing edge detection algorithm of edge vague, the pseudo-edge cannot be removed and algorithm results do not achieve optimal results by virtue. In order to improve the reliability and effectiveness of edge detection, the proposed optimization tool template coefficient method, to design the coding, Sobel filter and fitness function of genetic fuzzy clustering algorithm. Through interpolating, smooth handling and filtering with the updated active contour model. Based on the traditional edge detection algorithm is analyzed, combined with fuzzy membership functions and genetic operators for edge detection algorithm was improved by genetic fuzzy clustering. Through the simulation results showed that this new algorithm was feasible. Theoretical analysis and experimental results demonstrate that, the new algorithm in this paper is highly antinoise and able to get better image edges.

This paper presents a novel method to speed up neural network (NN) based face detection systems. NN-based face detection can be viewed as a classification and search problem. The proposed method formulates the search problem as an integer nonlinear optimization problem (INLP) and develops an improved genetic algorithm (IGA) to solve it. Each individual in the IGA represents a subwindow in an input image. The subwindows are evaluated by how well they match a NN-based face filter. A face is indicated when the filter response of the best particle is above a given threshold. Experimental results show that the proposed method leads to a speedup of 83 on $320{\times}240$ images compared to the traditional exhaustive search method.