Papers by Keyword: Particle Swarm Optimization (PSO)

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Authors: Li Fu Wang, Jian Ding, Zhi Kong
Abstract: A wireless sensor network (WSN) consists of spatially distributed wireless sensor nodes. The node power constrains the development of WSN. Employing techniques of clustering can reduce energy consumption of wireless sensor nodes and prolong the network lifetime. Therefore, in the study a new clustering routing algorithm is presented. The clustering algorithm uses the double-layer sensor nodes to communicate. And in order to optimize power energy consumption for WSN node energy, PSO algorithm is employed to find cluster head in each layer. Simulation results show that the algorithm not only can equal power energy of node, but also can reduce consumption in the long distance data transmission.
Authors: Hong Wang
Abstract: A Knapsack Problem is a typical NP complete problem. For solving Knapsack problem, A new improved Particle Swarm Optimization algorithm was proposed in this paper, the new algorithm combine Dantzigs theory of Knapsack Problem and crossover and mutation operation of Genetic Algorithm. According their fitness values, individuals are improved firstly by crossover, Daviss sequence crossover method and reverse mutation method are used respectively in the course of crossover and mutation. Numerical examples illustrate the validity and efficiency of the new hybrid Particle Swarm Optimization.
Authors: Xiang Tian, Yue Lin Gao
Abstract: This paper introduces the principles and characteristics of Particle Swarm Optimization algorithm, and aims at the shortcoming of PSO algorithm, which is easily plunging into the local minimum, then we proposes a new improved adaptive hybrid particle swarm optimization algorithm. It adopts dynamically changing inertia weight and variable learning factors, which is based on the mechanism of natural selection. The numerical results of classical functions illustrate that this hybrid algorithm improves global searching ability and the success rate.
Authors: Yu Guo Wu
Abstract: In order to raise the design efficiency and get the most excellent design effect, this paper combined Particle Swarm Optimization (PSO) algorithm and put forward a new kind of neural network, based on PSO algorithm and NARMA model. It gives the basic theory, steps and algorithm; The test results show that rapid global convergence and reached the lesser mean square error MSE) when compared with Genetic Algorithm, Simulated Annealing Algorithm, the BP algorithm with momentum term.
Authors: Zhong Xiao Cong, Peng Cheng Li, Jia Xiang Ou, Zhi Wei Peng
Abstract: Based on the research on the basis of analyzing the mechanism of polynomial fitting model, The polynomial fitting model or method was established based on intelligent optimization algorithm. The proposed method was applied to electric power system load forecasting, by a practical example’s calculation and analysis, this proposed intelligent optimization algorithm or method was verified to be feasible in the power system load forecasting, the results also showed that the method was compared with the traditional algorithm has superiority and has a broad application prospect in the field of polynomial fitting.
Authors: Tie Bin Wu, Yun Cheng, Yun Lian Liu, Tao Yun Zhou, Xin Jun Li
Abstract: Considering that the particle swarm optimization (PSO) algorithm has a tendency to get stuck at the local solutions, an improved PSO algorithm is proposed in this paper to solve constrained optimization problems. In this algorithm, the initial particle population is generated using good point set method such that the initial particles are uniformly distributed in the optimization domain. Then, during the optimization process, the particle population is divided into two sub-populations including feasible sub-population and infeasible sub-population. Finally, different crossover operations and mutation operations are applied for updating the particles in each of the two sub-populations. The effectiveness of the improved PSO algorithm is demonstrated on three benchmark functions.
Authors: Li Ping Xue, Ying Long Yao, Hong Zhou, Ping Cai
Abstract: The traditional training meshods of speaker codebook for speaker identification based on vector quantization are sensitive to the initial codebook parameters, and they often lead to a sub-optimal codebook in practice. To resolve this problem, this paper proposes a novel bi-group particle swarm optimizer (BPSO). It applies two sub-group particles with different particle update parameters simultaneously to explore the best speaker codebook, and the particles perform basic operations of particle swarm optimization (PSO) and conventional LBG algorithm in sequence, which can explore the solution space separately and search the local part in detail together. Information is exchanged when sub-groups are periodically shuffled and reorganized. Experimental results have demonstrated that the performance of BPSO is much better than that of LBG, fuzzy C-means (FCM), fast evolutionary programming (FEP), PSO, the impoved PSO algorithm consistently with higher correct identification rates and convergence rate. The dependence of the final codebook on the selection of the initial codebook is also reduced effectively.
Authors: Gui Yan Ding, Hao Liu, Xi Qin He
Abstract: Particle Swarm Optimization (PSO) has attracted many researchers attention to solve variant benchmark and real-world optimization problems because of its simplicity, effective performance and fast convergence. However, it suffers from premature convergence because of quickly losing diversity. To enhance its performance, this paper proposes a novel disruption strategy, originating from astrophysics, to shift the abilities between exploration and exploitation. The proposed Disruption PSO (DPSO) has been evaluated on a set of nonlinear benchmark functions and compared with other improved PSO. Comparison results confirm high performance of DPSO in solving various nonlinear functions.
Authors: Jian Ping Hu, Ya Lian Liu, Xing He, Jian Hua Wen
Abstract: The principle of Particle Swarm Optimization (PSO) and Homotopy Optimization (HO) is introduced. For the purpose of improving the local-searching efficiency of the PSO,HO-PSO is presented by combining the PSO with HO. New space homotopy curve produced by homotopy particle function directs the particle’s local optimization. For comparison,the methods of HO-PSO, PSO and FEM(Finite Element Method) are used to calculate the tunnel parameter inversion of the Shuibuya Project on the basis of the measured displacements. The result shows that HO-PSO takes smaller time compared with PSO and FEM in a same precision level, and the calculated values are in good agreement with the measured values,which also indicate that the HO-PSO can be well applied to the displacement back analysis in tunnel engineering.
Authors: Mei Jin Lin, Fei Luo, Yu Ge Xu, Long Luo
Abstract: Shuffled frog leaping algorithm (SFLA) is a meta-heuristic algorithm, which combines the social behavior technique and the global information exchange of memetic algorithms. But the SFLA has the shortcoming of low convergence speed while solving complex optimization problems. Particle swarm optimization (PSO) is a fast searching algorithms, but easily falls into the local optimum for the diversity scarcity of particles. In the paper, a new hybrid optimization called SFLA-PSO is proposed, which introduced PSO to SFLA by combining the fast search strategy of PSO and global search strategy of SFLA. Six benchmark functions are selected to compare the performance of SFLA-PSO, basic PSO, wPSO and SFLA. The simulation results show that the proposed algorithm SFLA-PSO possesses outstanding performance in the convergence speed and the precision of the global optimum solution.
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