Simultaneous Perturbation Particle Swarm Optimization

Maeda$^{, }$; Matsushita,; Miyoshi,; Hikawa,

doi:10.1109/cec.2006.1688375

Cited by 16 publications

(7 citation statements)

References 6 publications

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“…We directly combine the idea of particle swarm optimization and the simultaneous perturbation [14]. In this method, the momentum term of the particle swarm optimization is replaced by the simultaneous perturbation term.…”

Section: A Schemementioning

confidence: 99%

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On simultaneous perturbation particle swarm optimization

Maeda

Matsushita

Miyoshi

et al. 2009

2009 IEEE Congress on Evolutionary Computation

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In this paper, we describes the simultaneous perturbation particle swarm optimization which is a combination of the particle swarm optimization and the simultaneous perturbation optimization method. The method has global search capability of the particle swarm optimization and local search one of gradient method by the simultaneous perturbation. Some variations of the method are described. Comparison between these methods and the ordinary particle swarm optimization are shown through five test functions and learning problem of neural networks.

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Section: A Schemementioning

confidence: 99%

“…We have proposed a combination of the particle swarm optimization and the simultaneous perturbation method [14]. Combining the two methods, we have interesting new approach.…”

mentioning

confidence: 99%

On simultaneous perturbation particle swarm optimization

Maeda

Matsushita

Miyoshi

et al. 2009

2009 IEEE Congress on Evolutionary Computation

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show abstract

“…Therefore, combination of the particle swarm optimization and the simultaneous perturbation is promising and interesting approach. Combined methods of the particle swarm optimization and the simultaneous perturbation is proposed by Maeda (Maeda, 2006). In this work, the update algorithm which is a combination of particle swarm optimization and the simultaneous perturbation is applied for all the particles uniformly.…”

Section: Combination Of Particle Swarm Optimization and Simultaneous mentioning

confidence: 99%

Simultaneous Perturbation Particle Swarm Optimization and Its FPGA Implementation

Maeda¹,

Matsushita²

2009

Particle Swarm Optimization

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“…Recently Maeda and Kuratani in [28] has used SPSA with the bPSO in a hybrid algorithm called Simultaneous Perturbation PSO (SP-PSO) over a limited set of problems and reported some slight improvements over the bPSO. Both SP-PSO variants they proposed involved the insertion of the g k ( Â k ) directly over the velocity equations of all swarm particles with the intention of improving their local search capability.…”

Section: Spsa Overviewmentioning

confidence: 99%