Results 261 to 270 of about 3,109,206 (294)

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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Particle Swarm Optimization on FPGA

2018 30th International Conference on Microelectronics (ICM), 2018
The particle swarm optimization PSO is an attractive domain for community looking to enhance time for optimal solutions. Several works will be done for SW or SW/HW implementation. The latter often gives better performance. In this paper, we present a comparative study of these various solutions and we focus on the FPGA ones.
Monia Ettouil, Habib Smei, Abderrazak Jemai   +2 more
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Particle Swarms for Multimodal Optimization

2007
In this paper, five previous Particle Swarm Optimization (PSO) algorithms for multimodal function optimization are reviewed. A new and a successful PSO based algorithm, named as CPSO is proposed. CPSO enhances the exploration and exploitation capabilities of PSO by performing search using a random walk and a hill climbing components.
Ender Özcan, Murat Yilmaz
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A naive Particle Swarm Optimization

2012 IEEE Congress on Evolutionary Computation, 2012
Since the proposal of Particle Swarm Optimization (PSO), there have been many improvements of PSO which have not change the basic paradigm of PSO involving pattern of movement of particles, update mode of particles and algorithm framework. Instead of another improvement of PSO, a novel paradigm of PSO with more natural and simpler forms, called naive ...
Jin Qin, Zhenjun Liang
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Optimizing Particle Swarm Optimization algorithm

2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE), 2014
Particle Swarm Optimization (PSO) algorithm has become more popular recently. It has been shown to be an effective optimization tool in most of the applications. In this paper, we have applied the PSO algorithm to a sample Artificial Neural Network (ANN) application, measured the improvement, and optimized the PSO parameters to improve results as much ...
Iraj Koohi, Voicu Z. Groza
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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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The improvement of particle swarm optimization

2016 3rd International Conference on Systems and Informatics (ICSAI), 2016
Basic particle swarm optimization (PSO) has disadvantages about premature convergence. In order to solve these problems, this paper proposed an improved particle swarm optimization called DPSO. The velocity formula in basic PSO was divided into two parts and the two learning factors were improved.
Zekun Zhou, Bin Jiao
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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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Asynchronous Particle Swarm Optimization

2007 IEEE 15th Signal Processing and Communications Applications, 2007
In this article we consider an asynchronous particle swarm optimization (PSO) algorithm. In addition to the fact that the particles can update their estimates independently time delays are also allowed in obtaining information from their neighbors. Moreover, we test the performance of the algorithm with a simulation example.
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Alignment particle swarm optimization

2009 8th IEEE International Conference on Cognitive Informatics, 2009
Particle swarm optimization (PSO) simulates the boids' collective behaviors. The original biological background of boid should follow three basic simple steering behaviors: separation, alignment and cohesion. However, to promote a fast convergent speed, the velocity update manner of each boid omits the alignment rule, this may result premature ...
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