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The vibration signal is nonstationary and it is difficult to acquire the sample with typical fault. An improved ACO algorithm based on adaptive control parameters is introduced into SVM model to propose a new fault diagnosis (IMASFD) method in this paper. In the IMASFD method, the EMD method is used to decompose fault vibration signal into IMF components, the energy of IMF components is selected to construct the fault feature vectors. Then the adaptive controlling pheromone strategy, adaptive controlling stochastic selection threshold strategy and dynamic evaporation rate strategy are used to improve the basic ACO algorithm. The improved ACO algorithm is used to optimize the parameters of SVM model in order to obtain the optimal values of parameter combination in the SVM model. And a new fault diagnosis (IMASFD) method is proposed. Finally, the proposed IMASFD method is applied to the test data from bearing data center of CWRU. The experimental results show that the proposed method can accurately and effectively realize high precision fault diagnosis of rolling bearing, and has strong robustness and generalization ability, provides an effective method for realizing fault diagnosis of rolling bearing.

The basic ant colony optimization (ACO) algorithm takes on a longer computing time in the search process and is prone to fall into local optimal solutions, an improved ACO (CEULACO) algorithm is proposed in this paper. In the CEULAC algorithm, the direction guidance and real variable function are used to initialize pheromone concentration according to the path information of undirected graph. The pheromone dynamic evaporation rate strategy is proposed to control the pheromone evaporation in order to increase the global search capability and accelerate the convergence speed. An adaptive dynamic factor is introduced into pheromone updating rule to control the updating proportion of pheromone concentration with optimal solution in single iteration. And the local search strategy is used to improve the quality of the solution and select the current optimal path for global updating the pheromone in order to save some computing time and not reduce the searching efficiency. Some traveling salesman problems are selected to test the performance of the CEULACO algorithm. The simulation experiments show that the improved ACO algorithm can effectively improve the accuracy and the quality of solutions, and distinctly speed up the convergence speed and computing time.

In order to improve the search result and low evolution speed, and avoid the tendency towards stagnation and falling into the local optimum of ant colony optimization(ACO) in solving the complex function, the traditional ant colony optimization algorithm is analyzed in detail, an improved ant colony optimization(IWSMACO) algorithm based on information weight factor and supervisory mechanism is proposed in this paper. In the proposed IWSMACO algorithm, the information weight factor is added to the path selection and pheromone adjustment mechanisms in order to dynamically adjust path selection probability and randomly select the behavior rules for further intelligentializing the ant colony. The supervisory mechanism added the dynamic convergence criterion of supervisory distance and used the optimal pheromone update strategy to self-adaptively select the excellent ants for updating the pheromone trails, and improve the solution qualities of each iteration, better guide the later ants for learning. Finally, the proposed IWSMACO algorithm is carried out by 12 TSP instances. The simulation experiment results show that the proposed IWSMACO algorithm can not only avoid falling into the local optimum, but also enhance the convergence speed. And it takes on remarkable optimized ability and higher search accuracy.

In order to improve the global solving ability and convergence speed, avoid falling into local optimal solution, the basic ant colony optimization (ACO) algorithm is improved to propose an efficient and intelligent ant colony optimization (IMVPACO)algorithm. In the IMVPACO algorithm, the updating rules and adaptive adjustment strategy of pheromones are modify in order to better reflect the quality of the solution based on the increment of pheromone. The dynamic evaporation factor strategy is used to achieve the better balance between the solving efficiency and solving quality, and effectively avoid falling into local optimum for quickening the convergence speed. The movement rules of the ants are modify to make it adaptable for large-scale problem solving, optimize the path and improve search efficiency. A boundary symmetric mutation strategy is used to obtain the symmetric mutation for iteration results, which not only strengthens the mutation efficiency, but also improves the mutation quality. Finally, the proposed IMVPACO algorithm is applied in solving the traveling salesman problem. The simulation experiments show that the proposed IMVPACO algorithm can obtain very good results in finding optimal solution. And It takes on better global search ability and convergence performance than other traditional methods.

In order to improve the slow convergence speed and avoid falling into the local optimum in ant colony optimization algorithm, an improved quantum ant colony optimization (IMAQACO) algorithm based on combing quantum evolutionary algorithm with ant colony optimization algorithm is proposed for solving complex function problems in this paper. In the IMAQACO algorithm, the quantum state vectors are used to represent the pheromone, the adaptively dynamical updating strategy is used to control pheromone evaporation factor, the quantum rotation gate is used to realize the ant movement and change the convergence tend of quantum probability amplitude, quantum non-gate is used to realize ant location variation, so the IMAQACO algorithm has better global search ability and population diversity than ACO algorithm. In order to test the optimization performance of IMAQACO algorithm, several benchmark functions are selected in here. The tested results indicate that the IMAQACO can effectively improve the convergence speed and avoid falling into the local optimum, and has a stronger global optimization ability and higher convergence speed in solving complex function problems.

The ant colony algorithm is an algorithm which is used to find the optimal path. As a kind of bionic evolutionary algorithm, the ant colony algorithm is inspired by the real ant colony foraging mechanisms. Firstly, this paper introduces the basic model of the ant colony algorithm. Then, aiming at the shortcomings of the ant colony algorithm, we propose a new probability formula of the optimal path and the new formula of the pheromone update. In addition, we combine the traditional ant colony algorithm with the local search algorithm and propose the improved ant colony algorithm. It is the LSACA algorithm. In the experimental analysis, we set and analyze the parameters of the algorithm. Then, we compare with the traditional algorithm to prove the feasibility and the effectiveness of the algorithm.

In allusion to the shortcomings of easy falling into the local optimization and difficult obtaining Pareto optimal solutions for the original ant colony optimization algorithm in solving the complex optimization problems, multi-population, parallel mechanism, dynamic evaporation strategy and chaos theory are introduced into the original ant colony optimization algorithm in order to propose an improved multi-population ant colony optimization(MPPDCACO) algorithm in this paper. In the proposed MPPDCACO algorithm, the ant colony is divided into scout ants, search ants and worker ants in order to make the ACO algorithm as far as possible to avoid falling into local optimization and improve the local search ability of ant colony. The multi-population parallel mechanism is used to exchange the information and improve the computational effectiveness. The dynamic evaporation strategy is used to dynamically adjust the evaporation coefficient of pheromone in order to improve the global search capability of the ACO algorithm. The chaos theory is used to realize the optimization search in order to obtain the pheromone distributing in choosing path process. So the proposed MPPDCACO algorithm can prevent the local convergence caused by the misbalance of pheromone and can improve the searching ability. In order to test the optimization performance of the proposed MPPDCACO algorithm, 6 traveling salesman problems are selected from the TSPLIB in here. The experimental results show that the proposed MPPACACO algorithm takes on better global searching ability and higher convergence speed.

Vehicle navigation system can effectively alleviate traffic congestion, reduce pollution, and reduce travel cost and other problems. As is known to all, the traditional ones are all just static path planning with problems of not only weak effectiveness but also lack of standard optimal path options. They usually provide only one path which represents the shortest time or shortest distance, and ignore the actual demands of the dirivers. Based on traffic data of the past, the upcoming traffic flows can be estimated. With the help of the improved ant colony algorithm, the dynamic optimal path planning results will meet the need of the travelers according with multiple actual constraints.

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.