Papers by Keyword: Particle Swarm Optimization (PSO)

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Authors: Zhen Zhou An, Jin Rong Bai, Yu Ma, Na Yu Jia, Xin Ling Shi
Abstract: To study the organizational structure of particles in particle swarm optimization (PSO), we have proposed the family PSO (FPSO) previously. To further study the internal structure of FPSO, this paper introduced the animal collective behavior into the FPSO. It made the interaction ruling among particles was not based on random selection but topological distance. Each family interacted on average with a fixed number of neighbors, rather than with all neighbors within a fixed metric distance. Simulations for four benchmark functions demonstrated that the interaction ruling based on topological distance among particles was more reasonable than that on random selection.
Authors: Hong Wang, Pei Yi Zhao
Abstract: The Particle Swarm Optimization Algorithm is a new computational method for the combinatorial optimization problem, it is simple and effective, but it does suffer from the premature convergence. For overcome this problem and finding the optimal solution of the Stochastic Loader problem, we presented a new hybrid Particle Swarm Optimization Algorithm that combines with Artificial Immune Algorithm, such as immune memory, antibody promotion and suppression, immune selection and so on. Numerical example illustrates the higher efficiency and reliability of the new hybrid PSO compared with the basic Particle Swarm Optimization Algorithm.
Authors: Ning Qiang, Feng Ju Kang
Abstract: As one of the most popular supply chain management problems, the Vehicle Routing Problem (VRP) has been thoroughly studied in the last decades, most of these studies focus on deterministic problem where the customer demands are known in advance. But the Vehicle Routing Problem with Stochastic Demands (VRPSD) has not received enough consideration. In the VRPSD, the vehicle does not know the customer demands until the vehicle arrive to them. This paper use a hybrid algorithm for solving VRPSD, the hybrid algorithm based on Particle Swarm Optimization (PSO) Algorithm, combines a Greedy Randomized Adaptive Search Procedure (GRASP) algorithm, and Variable Neighborhood Search (VNS) algorithm. A real number encoding method is designed to build a suitable mapping between solutions of problem and particles in PSO. A number of computational studies, along with comparisons with other existing algorithms, showed that the proposed hybrid algorithm is a feasible and effective approach for Vehicle Routing Problem with Stochastic Demands.
Authors: Can Tao Shi, Ting Li, Yu Zhuo Liu
Abstract: With consideration of three important product types, cold trip, hot strip, and slab, this paper concentrates on the integrated surplus products matching problem (ISPMP) in iron and steel enterprises. A mathematical model is built to maximize satisfied demand and the amount of fulfilled orders while minimizing total involved cost. A PSO based algorithm is proposed into which a neighborhood search procedure is embedded to speed up the convergence. The computational tests show that the hybrid algorithm has good performance.
Authors: Sun Xin Wang, Yan Li, Yan Rong Zhang
Abstract: In this paper a hybrid algorithm named IPSO-VND is proposed and applied to solving the vehicle routing problem with simultaneous pickup and delivery (VRPSPD). The IPSO-VND algorithm combines two meta-heuristics: Improved Particle Swarm Optimization (IPSO) is used to find a group of excellent solutions, and then the Variable Neighborhood Descent (VND) is implemented to deeply search to achieve the optimal solution around these solutions. During the IPSO procedure, in order to make up for the change of a particle’s position, a velocity component is added to the movement of any particle which has been optimized or made feasible. During the VND procedure, three different neighborhood structures: insertion, swap and cross are successively used. Computational results on the benchmark problems show that our IPSO-VND algorithm is effective.
Authors: Te Jen Su, Jui Chuan Cheng, Ming Yuan Huang, Xun Xain Zhan
Abstract: This paper presents a smart-routing mechanism of a control system to track Low Earth Orbit (LEO) satellites. Satellite tracking mainly relies on the antenna pointing database generated by SGP4 orbit forecasting model and follow the point coordinates to command the rotation of the axes. Gears rotation gaps will affect the strength of the received signal; the Proportional Integral (PI) controller is used to adjust the error values caused by the drive shaft mechanism. Particle swarm optimization (PSO) algorithm has fewer parameter settings and the advantages of fast convergence, which is adopted for variable selection and optimization for the parameters kp and ki of PI controller. The resolver feedback mechanism of actual angle indicator is using as a basis for performance adjustment in the search process. The experimental results of a three axes tracking system demonstrate the reliability and better performance of the proposed PSO-PI satellite tracking system.
Authors: Tie Bin Wu, Yun Cheng, Zhi Kun Hu, Tao Yun Zhou, Yun Lian Liu
Abstract: For the issues of inferior local search ability and premature convergence in the later evolution stage of the traditional particle swarm optimization, an improved particle swarm optimization is proposed and applied to the parameter estimation. Firstly, in the evolutionary process of particle swarm optimization, the particles which have crossed the border are buffered according to the speed. Then, each particle is performed mutation in different probability according to the evolutionary generations, which can keep the diversity of the particle swarm and avoid the premature convergence effectively. Thirdly, a crossover operation is conducted between the current best particle and the particle which is selected from the particle swarm with a certain probability, which can lead particles gradually approaching to the extreme point and hence, the local search ability of the algorithm will be improved. The advanced particle swarm optimization is applied to the parameter estimation of the kinetic model in the Hg oxidation process and the application result show the effectiveness of the suggested algorithm.
Authors: Jun Hui Pan, Hui Wang, Pan Chi Li
Abstract: To improve the optimization performance of particle swarm, an adaptive quantum particle swarm optimization algorithm is proposed. In the algorithm, the spatial position of particles is described by the phase of quantum bits, and the position mutation of particles is achieved by Pauli-Z gates. An adaptive determination method of the global-factors is proposed by studying the relationship among inertia factors, self-factors and global-factors. The experimental results demonstrate that the proposed algorithm is much better than the standard particle swarm algorithm by solving the function extremum optimization problems.
Authors: Chao Chen, Shi Jie Zhou, Jia Qing Luo, Yan Pan Chen
Abstract: In an intensive RFID reader environment, multiple RFID reader are deployed together to cover a pointed area. In such intensive RFID reader application, it needs to determine how many readers are enough to cover the expect area and calculate the position of readers. However, the coverage of multiple readers is a NP problem. Therefore, it needs an approximate approach to optimize the coverage. In this paper, we proposed a lattice decentralized approach to model the coverage problem of intensive RFID reader deployment. In our novel model, both the deployment area and the reader reading region are discretized to a lattice and described by a matrix. Then, the coverage is easily calculated by matrix operation. In order to test our discrete method, we propose a heuristic algorithm to deploy readers based on the PSO (particle swarm optimization) algorithm. The simulations show that the proposed algorithm can cover an irregular or regular area with a high coverage rate and a low overlapping rate.
Authors: Jin Quan Zheng, Ya Jun Wang
Abstract: In tone reservation-based OFDM systems, the peak to average power ratio (PAPR) reduction performance mainly depends on the selection of peak reduction tone (PRT) set. it is known that finding the optimal PRT set requires an exhaustive search of all combination of possible PRT sets, and this search is infeasible for the number of tones used in practical systems. The existing selection methods perform poorly or incur high computational complexity. In this paper, an efficient scheme based on particle swarm optimization algorithm (PSO) is proposed to search the nearly optimal PRT set.Simulation results show that our proposed algorithm can obtain a nearly optimal PRT set and good PAPR reductions.
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