Search

To improve the efficiency of particle swarm optimization (PSO) used in traffic signal timing, some methods were talked about. In this paper, a new method Particle Swarm Optimization Algorithm with Global Best Gradient Descent was proposed. Not only can this algorithm avoid the optimal value getting into a local extreme, but also simple and efficient. Through comparing the simulation results of the mentioned methods in this paper, the conclusion was drawn. Simulation results show that this algorithm can get a higher efficiency when applied to port traffic signal timing compared with other intelligence methods. [ABSTRACT FROM PUBLISHER]

In this paper, a predictive control algorithm is presented based on Type-2 fuzzy model and chaotic particle swarm optimization (CPSO) algorithm: using the Type-2 T-S fuzzy model as predictive model, and using CPSO algorithm to solve the optimization index, so the predictive control can avoid complex gradient calculation and matrix inversion, and can quickly to search the optimal solution. Furthermore, in order to improve the effectiveness of Type-2 model, the modified G-K clustering method is applied to determine the clustering number c and clustering centers. For a numerical example, the simulation results show the effectiveness of the algorithm proposed in this paper. [ABSTRACT FROM PUBLISHER]

This paper introduces a parameter-automatically-optimized robust graph-based image segmentation method (PAORGB) for segmenting breast tumors in ultrasonic images. The robust graph-based (RGB) segmentation algorithm is based on the minimum spanning trees in a graph generated from an image. However, the values of k and α, which are two significant parameters in the RGB algorithm, are empirically selected in the reported studies. In this paper, we propose the PAORGB method, based on the particle swarm optimization algorithm to suitably set k and α, so as to overcome the problem of under-segmentation or over-segmentation in the RGB segmentation algorithm. Experimental results have shown that the proposed segmentation algorithm can successfully and more accurately detect tumors and extract lesions in ultrasound images in comparison with the RGB with default parameter settings and the Fuzzy C means clustering. [ABSTRACT FROM PUBLISHER]

Considering the characteristics of sintering burden process and the requirements of energy-saving and environment protection for iron and steel industry. This paper presents a multi-objective optimization algorithm based on fuzzy satisfaction. Firstly, the fuzzy satisfactory functions that can describe the actual satisfaction about the cost and sulfur contained are built, and then a multi-objective optimization model is established in which cost and emission is considered to maximize the minimum satisfaction degree. Secondly, for the nonlinear and multivariable features of this model, a BC-PSO algorithm is proposed by combining the extensive mapping ability of particle swarm optimization (PSO) algorithm and the strong local search ability of bee colony (BC) algorithm. The practical data based simulation indicates that the BC-PSO algorithm can work efficiently on cost decreasing and emission reduction. [ABSTRACT FROM PUBLISHER]

In this paper, Fuzzy C-Means (FCM) is adopted to constitute multiple classifiers group to classify the bearing failure and its clustering centers are optimized by Particle Swarm Optimization (PSO) algorithm with global optimization and fast convergence characteristics. Classification recognition rate obtained by FCM is integrated by fuzzy integral information fusion system to gain the final result, in which fuzzy measure also is optimized by the PSO algorithm. Simulation of identifying the bearing inner race, outer race and rolling bodies fault, the results show that the classifier improves the recognition accuracy rate of the fault diagnosis. [ABSTRACT FROM PUBLISHER]

This paper presents the design of a robust fractional order controller for unstable plants with only one unstable pole based on quantitative feedback theory (QFT) using Particle Swarm Optimization (PSO). In this work, a fractional-order compensator, with a flexible controller structure, is introduced into the QFT design to give a better approximation of optimum open loop in Nichols. The fractional order controller is designed for not only minimum phase system but also uncertain non-minimum phase and unstable plants in QFT. It has been demonstrated that the fractional order controller can provide smaller high frequency gain than the integer order controller due to its extra tunable parameters. [ABSTRACT FROM PUBLISHER]

Power industry is facing a rapid development toward larger-capacity supercritical and ultra-supercritical coal-fired power generating units. The big inertia, large time delay and nonlinear characteristics of the superheater system for a supercritical boiler are becoming more and more obvious. The conventional cascaded PID control scheme is often difficult to obtain satisfactory steam temperature control effect over wide-range loading conditions. In this paper, an intelligent predictive optimal controller based on neural network (NN) modeling and Particle Swarm Optimization (PSO) is presented for superheated steam temperature control. Aiming at the known drawbacks of the NN predictive optimal controller, a time-delay BP neural network is used to establish the nonlinear dynamic model of the superheater system to improve the model's real-time prediction accuracy and generalization ability. A new simplified high-efficiency PSO algorithm discarding the concept of velocity is adopted to search the optimal controls with faster convergence rate to meet the real-time control demand. An elastic search space is updated dynamically based on the real-time steam temperature control error to prevent oscillation and to enhance stability. By taking a full-scope simulator of a 600MW supercritical power unit as the object investigated, the neural network model of the superheater system is built and trained with historical wide-range operating data. The new control scheme is programmed with MATLAB software and it communicates two-way with the simulator. Extensive control simulation tests are made. It is shown the proposed intelligent predictive optimal control scheme can greatly improve the superheated steam temperature control with good application prospect. [ABSTRACT FROM PUBLISHER]

To the shortcoming of BP algorithm that solution is sensitive to initial value and easy to trap in local optima, this paper makes a research on Particle Swarm Optimization (PSO), Chaos Optimization Algorithm (COA) and a modified Chaos Particle Swarm Optimization (CPSO) and applies them in neural networks learning problem. The mechanism of algorithms is explored in depth. A novel method of evaluating the degree of gathering for the swarm is proposed. The performance of algorithms is tested and analyzed by simulation and compared with BP algorithm. The results show that as novel neural networks learning algorithms, PSO and CPSO can overcome the defect of BP algorithm whose solution is sensitive to initial value and have the certain application value. [ABSTRACT FROM PUBLISHER]

This paper presents the design of a robust fractional order controller for non-minimum phase hydro power plants based on quantitative feedback theory (QFT) using Particle Swarm Optimization (PSO). In this work, a fractional-order compensator, with a flexible controller structure, is introduced into the QFT design to give a better approximation of optimum open loop in Nichols. It has been demonstrated that the fractional order controller can provide smaller high frequency gain than the integer order controller due to its extra tunable parameters. [ABSTRACT FROM PUBLISHER]

This paper Aims at the problem that the particle swarm optimization algorithm(PSO) is easy to pass into the local minimum and the weakness of hybrid particle swarm optimization algorithm often uses too much iterations and consumes a long time, and puts forward a parallel collaborative optimization algorithms that combine PSO searching and chaotic searching together through using the advantages that particle swarm optimization algorithm can converge rapidly and the tent sequences have much better ergodicity. Parallel collaborative optimization algorithm utilizes PSO algorithm and chaotic searching respectively for global searching and local searching and reduces the number of iterations and the time consumption for searching through information exchange and sharing under the premise of ensuring the ability of global searching raises. Testing shows: the parallel collaborative optimization algorithm not only has a better optimization ability than originally algorithm, but although can find the optimal solution with the less iterations and use less time to complete the process of searching the best solution. [ABSTRACT FROM PUBLISHER]

A novel cultural algorithm based on particle swarm optimization (PSO) algorithm was proposed in this paper. After analyzing the partner selection problems of virtual enterprise, the CPSO algorithm was presented to solve enterprise alliance problem within reasonable time and cost. There are certain number partners of each sub-task in virtual enterprise environment. The objective is, by selecting the optimal combination of partners, to minimize project's completion time and project's total cost. We tested the CPSO algorithm against the PSO method. Simulation results demonstrate that it can be superior to the regular PSO. We also tested the CPSO algorithm with the exhaustion method to show the algorithm's efficiency. [ABSTRACT FROM PUBLISHER]

This paper investigates an optimal design of composite nonlinear feedback control law for a gantry crane system. The control objective is to drive the trolley of the gantry crane system to a target position as fast as possible without any overshoot while the load is limited in a specified swaying angle. An optimal linear feedback control problem and an optimal composite nonlinear feedback control problem are respectively formulated as parameter optimization problems under the same performance index and restrictions on stability, tracking overshoot, and swaying angle. To handle the multi-restrictions, the particle swarm optimization algorithm is applied to solve the parameter optimization problems. The transient performance under the optimal linear feedback control and the optimal composite nonlinear feedback control are compared by applying the designed control laws to a gantry crane system. [ABSTRACT FROM PUBLISHER]