Results 261 to 270 of about 82,097 (301)

Chaotic particle swarm optimization

Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation, 2009
A new particle swarm optimization (PSO) algorithm with has a chaotic neural network structure, is proposed. The structure is similar to the Hopfield neural network with transient chaos, and has an improved ability to search for globally optimal solution and does not suffer from problems of premature convergence. The presented PSO model is discrete-time
Yanxia Sun 0001, Guoyuan Qi, Zenghui Wang 0001, Barend J. van Wyk, Yskandar Hamam   +4 more
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Dispersed particle swarm optimization

Information Processing Letters, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xingjuan Cai, Zhihua Cui, Jianchao Zeng 0001, Ying Tan 0003   +3 more
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Multiobjective particle swarm optimization

Proceedings of the 38th annual on Southeast regional conference, 2000
Evolutionary algorithms (EAs) are search procedures based on natural selection [2]. They have been successfully applied to a wide variety of optimization problems [4]. Particle Swarm Optimization (PSO) [1,7] is a new type of evolutionary paradigm that has been successfully used to solve a number of single objective optimization problems (SOPs). However,
Jacqueline Moore, Richard Chapman 0001, Gerry V. Dozier   +2 more
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Engineering optimization with particle swarm

Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706), 2004
The paper presents a modified particle swarm optimization (PSO) algorithm for engineering optimization problems with constraints. PSO is started with a group of feasible solutions and a feasibility function is used to check if the newly explored solutions satisfy all the constraints. All the particles keep only those feasible solutions in their memory.
Xiaohui Hu, Russell C. Eberhart, Yuhui Shi 0001   +2 more
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Vortex Particle Swarm Optimization

2013 IEEE Congress on Evolutionary Computation, 2013
This paper presents an optimization algorithm based on self-propelled particle swarms which exploit vorticity features in order to avoid local minima; the proposed algorithm is termed Vortex Particle Swarm Optimization (VPSO). The optimization algorithm switches between translational and dispersion behavior of the swarm to enhance the exploration of ...
Helbert E. Espitia, Jorge I. Sofrony
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Biases in Particle Swarm Optimization

International Journal of Swarm Intelligence Research, 2010
The most common versions of particle swarm optimization (PSO) algorithms are rotationally variant. It has also been pointed out that PSO algorithms can concentrate particles along paths parallel to the coordinate axes. In this paper, the authors explicitly connect these two observations by showing that the rotational variance is related to the ...
William M. Spears, Derek T. Green, Diana F. Spears   +2 more
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Emotional Particle Swarm Optimization

2009
It is known that there is only information sharing in most particle swarm optimization. But competition among particles which is a good feature for searching progress does not exist. For all these, based on the idea of multiagent with emotion, bring in competition controlled by emotion to enhance performance of PSO after describing similarity between ...
Wei Wang 0162, Zhiliang Wang, Xuejing Gu, Siyi Zheng   +3 more
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The Thermodynamic Particle Swarm Optimizer

2008 International Conference on Computer Science and Software Engineering, 2008
This paper has presented a novel optimization algorithm - thermodynamic particle swarm optimizers (TDPSO). It combines the simplified evolutionary equation and the thermodynamically strategy.The simplified equation without the velocity variable has drastically reduced computation costs to achieve faster convergence.
Yu Wu, Yuanxiang Li, Xing Xu, Shen Peng
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Predicted Particle Swarm Optimization

2006 5th IEEE International Conference on Cognitive Informatics, 2006
The standard particle swarm optimization (PSO) may prematurely converge on suboptimal solution partly because of the insufficiency information utilization of the velocity. The time cost by velocity is longer than position of each particle of the swarm, though the velocity, limited by the constant vmax, only provides the positional displacement.
Zhihua Cui, Jianchao Zeng 0001, Guoji Sun   +2 more
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Topology Optimization of Particle Swarm Optimization

2014
Particle Swarm Optimization (PSO) is popular in optimization problems for its quick convergence and simple realization. The topology of standard PSO is global-coupling and likely to stop at local optima rather than the global one. This paper analyses PSO topology with complex network theory and proposes two approaches to improve PSO performance.
Fenglin Li, Jian Guo 0006
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