The Experts below are selected from a list of 185247 Experts worldwide ranked by ideXlab platform
Qiming Li - One of the best experts on this subject based on the ideXlab platform.
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an adaptive cultural algorithm with improved quantum behaved Particle Swarm Optimization for sonar image detection
Scientific Reports, 2017Co-Authors: Xingmei Wang, Qiming LiAbstract:This paper proposes an adaptive cultural algorithm with improved quantum-behaved Particle Swarm Optimization (ACA-IQPSO) to detect the underwater sonar image. In the population space, to improve searching ability of Particles, iterative times and the fitness value of Particles are regarded as factors to adaptively adjust the contraction-expansion coefficient of the quantum-behaved Particle Swarm Optimization algorithm (QPSO). The improved quantum-behaved Particle Swarm Optimization algorithm (IQPSO) can make Particles adjust their behaviours according to their quality. In the belief space, a new update strategy is adopted to update cultural individuals according to the idea of the update strategy in shuffled frog leaping algorithm (SFLA). Moreover, to enhance the utilization of information in the population space and belief space, accept function and influence function are redesigned in the new communication protocol. The experimental results show that ACA-IQPSO can obtain good clustering centres according to the grey distribution information of underwater sonar images, and accurately complete underwater objects detection. Compared with other algorithms, the proposed ACA-IQPSO has good effectiveness, excellent adaptability, a powerful searching ability and high convergence efficiency. Meanwhile, the experimental results of the benchmark functions can further demonstrate that the proposed ACA-IQPSO has better searching ability, convergence efficiency and stability.
Yalan Zhou - One of the best experts on this subject based on the ideXlab platform.
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discrete Particle Swarm Optimization based on estimation of distribution for polygonal approximation problems
Expert Systems With Applications, 2009Co-Authors: Jiahai Wang, Zhanghui Kuang, Xinshun Xu, Yalan ZhouAbstract:The polygonal approximation is an important topic in the area of pattern recognition, computer graphics and computer vision. This paper presents a novel discrete Particle Swarm Optimization algorithm based on estimation of distribution (DPSO-EDA), for two types of polygonal approximation problems. Estimation of distribution algorithms sample new solutions from a probability model which characterizes the distribution of promising solutions in the search space at each generation. The DPSO-EDA incorporates the global statistical information collected from local best solution of all Particles into the Particle Swarm Optimization and therefore each Particle has comprehensive learning and search ability. Further, constraint handling methods based on the split-and-merge local search is introduced to satisfy the constraints of the two types of problems. Simulation results on several benchmark problems show that the DPSO-EDA is better than previous methods such as genetic algorithm, tabu search, Particle Swarm Optimization, and ant colony Optimization.
Yoshikazu Fukuyama - One of the best experts on this subject based on the ideXlab platform.
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a hybrid Particle Swarm Optimization for distribution state estimation
2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491), 2003Co-Authors: Shigenori Naka, T Genji, T Yura, Yoshikazu FukuyamaAbstract:This paper proposes a hybrid Particle Swarm Optimization for a practical distribution state estimation. The proposed method considers nonlinear characteristics of the practical equipment and actual limited measurements in distribution systems. The method can estimate load and distributed generation output values at each node by minimizing difference between measured and calculated voltages and currents. The feasibility of the proposed method is demonstrated and compared with an original Particle Swarm Optimization based method on practical distribution system models. Effectiveness of the constriction factor approach of Particle Swarm Optimization is also investigated. The results indicate the applicability of the proposed state estimation method to the practical distribution systems.
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a hybrid Particle Swarm Optimization for distribution state estimation
IEEE Transactions on Power Systems, 2002Co-Authors: Shigenori Naka, T Genji, T Yura, Yoshikazu FukuyamaAbstract:This paper proposes a hybrid Particle Swarm Optimization (HPSO) for a practical distribution state estimation. The proposed method considers nonlinear characteristics of the practical equipment and actual limited measurements in distribution systems. The method can estimate load and distributed generation output values at each node by minimizing the difference between measured and calculated voltages and currents. The feasibility of the proposed method is demonstrated and compared with an original Particle Swarm Optimization-based method on practical distribution system models. Effectiveness of the constriction factor approach of Particle Swarm Optimization is also investigated. The results indicate the applicability of the proposed state estimation method to the practical distribution systems.
Federico Marini - One of the best experts on this subject based on the ideXlab platform.
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Particle Swarm Optimization pso a tutorial
Chemometrics and Intelligent Laboratory Systems, 2015Co-Authors: Federico MariniAbstract:Abstract Swarm-based algorithms emerged as a powerful family of Optimization techniques, inspired by the collective behavior of social animals. In Particle Swarm Optimization (PSO) the set of candidate solutions to the Optimization problem is defined as a Swarm of Particles which may flow through the parameter space defining trajectories which are driven by their own and neighbors' best performances. In the present paper, the potential of Particle Swarm Optimization for solving various kinds of Optimization problems in chemometrics is shown through an extensive description of the algorithm (highlighting the importance of the proper choice of its metaparameters) and by means of selected worked examples in the fields of signal warping, estimation robust PCA solutions and variable selection.
Xingmei Wang - One of the best experts on this subject based on the ideXlab platform.
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an adaptive cultural algorithm with improved quantum behaved Particle Swarm Optimization for sonar image detection
Scientific Reports, 2017Co-Authors: Xingmei Wang, Qiming LiAbstract:This paper proposes an adaptive cultural algorithm with improved quantum-behaved Particle Swarm Optimization (ACA-IQPSO) to detect the underwater sonar image. In the population space, to improve searching ability of Particles, iterative times and the fitness value of Particles are regarded as factors to adaptively adjust the contraction-expansion coefficient of the quantum-behaved Particle Swarm Optimization algorithm (QPSO). The improved quantum-behaved Particle Swarm Optimization algorithm (IQPSO) can make Particles adjust their behaviours according to their quality. In the belief space, a new update strategy is adopted to update cultural individuals according to the idea of the update strategy in shuffled frog leaping algorithm (SFLA). Moreover, to enhance the utilization of information in the population space and belief space, accept function and influence function are redesigned in the new communication protocol. The experimental results show that ACA-IQPSO can obtain good clustering centres according to the grey distribution information of underwater sonar images, and accurately complete underwater objects detection. Compared with other algorithms, the proposed ACA-IQPSO has good effectiveness, excellent adaptability, a powerful searching ability and high convergence efficiency. Meanwhile, the experimental results of the benchmark functions can further demonstrate that the proposed ACA-IQPSO has better searching ability, convergence efficiency and stability.