Papers by Keyword: Particle Swarm Optimization (PSO)

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Authors: Lei Chen
Abstract: Particle swarm optimization (PSO) is a global algorithm which is inspired by birds flocking and fish schooling. PSO has shown good search ability in many complex optimization problems, but premature convergence is still a main problem. A novel hybrid PSO(NHPSO) was proposed, which employed hybrid strategies, including dynamic step length (DSL) and opposition-based learning (OBL). DSL is helpful to enhance local search ability of PSO, and OBL is beneficial for improving the quality of candidate solutions. In order to verify the performance of NHPSO, we test it on several benchmark functions. The simulation results demonstrate the effectiveness and efficiency of our approach.
Authors: Shuang Liu, Jing Wen Xu, Jun Fang Zhao, Yong Li
Abstract: Conventional process-based rainfall-runoff models are difficult to catch the non-linear factors and to take full advantages of previous information of rainfall and runoff. However, these factors are closely related to the initial watershed average saturation deficit at each time step. Therefore, in order to address the issue, this study selected the parameter about initial underground flow in TOPMODEL (TOPOgraphic driven Model) as the breakthrough point. Then we used the previous two-day observed runoff and rainfall data as the inputs of an artificial neural network (ANN) and initial subsurface flow of present day as an output, then integrated ANN into runoff generation module in TOPMODEL and finally applied the integrated model for daily runoff modeling in Yingluoxia watershed with 10009km2, China. In addition, this work also utilized particle swarm optimization technique (PSO) to avoid the local optimization, especially for the integration of black-box and physical models. The result shows that during the validation period the Nash-Sutcliffe efficiency coefficient (NE) and root mean square error (RMSE) of TOPMODEL are 0.45 and 3.88×10-4m respectively while the NE of 0.70 and RMSE of 2.85×10-4m for the integrated model. Significantly, the integrated model performs much better than the traditional model. Hence, this new method of integrating ANN with the runoff generation module of TOPMODEL is promising and easily extended to other process-based rainfall-runoff models as well.
Authors: Qian Ru Wang, Xi Wei Chen, Da Shi Luo, Yu Feng Wei, Li Ya Jin, Li Liu
Abstract: Grey system theory has been widely used to forecast the economic data that are often highly nonlinear, irregular and non-stationary. Many models based on grey system theory could adapt to various economic time series data. However, some of these models didnt consider the impact of the model parameters, or only considered a simple change of the model parameters for the prediction. In this paper, we proposed the PSO based GM (1, 1) model using the optimized parameters in order to improve the forecasting accuracy. The experiment shows that PSO based GM (1, 1) gets much better forecasting accuracy compared with other widely used grey models on the actual chaotic economic data.
Authors: Yi Zuo, Yong Chen
Abstract: Recently, Particle Swarm Optimization (PSO) has received increasing interest from the optimization community due to its simplicity in implementation and its high speed computational capability. However, traditional PSO has premature convergence problem. To prevent the premature convergence of PSO, some modified algorithms is proposed, based on these modified algorithms a novel hybrid algorithm with random disturbance is proposed in this paper. The performance of the proposed algorithm is testified on a suite of benchmark functions. The simulation results show that this hybridized PSO has great capability of preventing premature convergence and faster computing speed.
Authors: Shao Rong Huang
Abstract: To improve the performance of standard particle swarm optimization algorithm that is easily trapped in local optimum, based on analyzing and comparing with all kinds of algorithm parameter settings strategy, this paper proposed a novel particle swarm optimization algorithm which the inertia weight (ω) and acceleration coefficients (c1 and c2) are generated as random numbers within a certain range in each iteration process. The experimental results show that the new method is valid with a high precision and a fast convergence rate.
Authors: Fei Hong Zhou, Zi Zhen Liao
Abstract: The basic and improved algorithms of PSO focus on how to effectively search the optimal solution in the solution space using one of the particle swarm. However, the particles are always chasing the global optimal point and such points currently found on their way of search, rapidly leading their speed down to zero and hence being restrained in the local minimum. Consequently, the convergence or early maturity of particles exists. The improved PSO is based on the enlightenment of BP neural network while the improvement is similar to smooth the weight through low-pass filter. The test of classical functions show that the PSO provides a promotion in the convergence precision and calculation velocity to a certain extent.
Authors: Zi Chao Yan, Yang Shen Luo
Abstract: The passage aims at solving the problems resulted from the optimized process of Particle Swarm Optimization (PSO), which might reduce the population diversity, cause the algorithm to convergence too early, etc. A whole new mutable simulated annealing particle swarm optimization is proposed based on the combine of the simulated annealing mechanism and mutation. This new algorithm substitutes the Metropolis criterion in the simulated annealing mechanism for mutagenic factors in the process of mutation, which both ensures the diversity of the particle swarm, and ameliorates the quality of the swarm, so that this algorithm would convergence to the global optimum. According to the result of simulated analysis, this hybrid algorithm maintains the simplicity of the particle swarm optimization, improves its capability of global optimization, and finally accelerates the convergence and enhances the precision of this algorithm.
Authors: Jun Li Zhang, Da Wei Dai
Abstract: For the purpose of overcoming the premature property and low execution efficiency of the Particle Swarm Optimization (PSO) algorithm, this paper presents a particle swarm optimization algorithm based on the pattern search. In this algorithm, personal and global optimum particles are chosen in every iteration by a probability. Then, local optimization will be performed by the pattern search and then the original individuals will be replaced. The strong local search function of the pattern search provides an effective mechanism for the PSO algorithm to escape from the local optimum, which avoids prematurity of the algorithm. Simulation shows that this algorithm features a stronger function of global search than conventional PSO, so that the optimization process can be improved remarkably.
Authors: Hai Sheng Qin, Deng Yue Wei, Jun Hui Li, Lei Zhang, Yan Qiang Feng
Abstract: A new particle swarm optimization (PSO) algorithm (a PSO with Variety Factor, VFPSO) based on the PSO was proposed. Compared with the previous algorithm, the proposed algorithm is to update the Variety Factor and to improve the inertia weight of the PSO. The target of the improvement is that the new algorithm could go on enhancing the robustness as before and should reduce the risk of premature convergence. The simulation experiments show that it has great advantages of convergence property over some other modified PSO algorithms, and also avoids algorithm being trapped in local minimum effectively. So it can avoid the phenomenon of premature convergence.
Authors: Jun Hui Pan, Hui Wang, Xiao Gang Yang
Abstract: To improve the efficiency of particle swarm optimization, a random particle swarm optimization algorithm is proposed on the basis of analyzing the search process of quantum particle swarm optimization algorithm. The proposed algorithm has only a parameter, and its search step length is controlled by a random variable value. In this model, the target position can be accurately tracked by the reasonable design of the control parameter. The experimental results of standard test function extreme optimization and clustering optimization show that the proposed algorithm is superior to the quantum particle swarm optimization and the common particle swarm optimization algorithm in optimization ability and optimization efficiency.
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