On simultaneous perturbation particle swarm optimization

Maeda, Yutaka; Matsushita, N.; Miyoshi, Seiji; Hikawa, Hiroomi

doi:10.1109/cec.2009.4983359

Cited by 10 publications

(6 citation statements)

References 14 publications

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“…J.Alespector et al and G.Cauwenberghs also proposed a parallel gradient descent method and stochastic errordescent algorithm, respectively, which are identical to the simultaneous perturbation learning rule [8,9]. Many applications of the simultaneous perturbation are reported in the fields of neural networks [10] and their hardware implementation [11,12].…”

Section: Simultaneous Perturbation Methodsmentioning

confidence: 97%

“…Combined methods of particle swarm optimization and the simultaneous perturbation is proposed by Y.Maeda [12]. 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: Simultaneous Perturbationmentioning

confidence: 99%

“…We directly combine the idea of particle swarm optimization and the simultaneous perturbation [12,13]. In this method, the momentum term of Eq.…”

Section: A Schemementioning

confidence: 99%

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Combination of particle swarm optimization and simultaneous perturbation

Maeda

Matsushita

2008

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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In this paper, we propose some different optimization schemes which are combinations of the particle swarm optimization and the simultaneous perturbation optimization method. The proposed schemes can utilize local information of an objective function and global shape of the function at the same time. These characteristics are from the simultaneous perturbation optimization method and the particle swarm optimization. The schemes have good properties of global search and efficient local search capability. Moreover, the schemes themselves are very simple and easy to implement. These methods only require values of the function similar to the original particle swarm optimization and the simultaneous perturbation method. The proposed schemes are investigated using some test function to know convergence properties such as convergence rate or convergence speed.

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Section: Simultaneous Perturbation Methodsmentioning

confidence: 97%

Section: Simultaneous Perturbationmentioning

confidence: 99%

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Combination of particle swarm optimization and simultaneous perturbation

Maeda

Matsushita

2008

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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“…However, in standard PSO, the local information of an objective function is not used, such as gradient (Maeda and Matsushita, 2008; Maeda et al , 2009). This means even if a search point of PSO exists around the optimal value, particle passes the point, as local information is not considered.…”

Section: Mfastslam Using Soft Computingmentioning

confidence: 99%

A mutated FastSLAM using soft computing

Havangi

2017

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Purpose Simultaneous localization and mapping (SLAM) is the problem of determining the pose (position and orientation) of an autonomous robot moving through an unknown environment. The classical FastSLAM is a well-known solution to SLAM. In FastSLAM, a particle filter is used for the robot pose estimation, and the Kalman filter (KF) is used for the feature location’s estimation. However, the performance of the conventional FastSLAM is inconsistent. To tackle this problem, this study aims to propose a mutated FastSLAM (MFastSLAM) using soft computing. Design/methodology/approach The proposed method uses soft computing. In this approach, particle swarm optimization (PSO) estimator is used for the robot’s pose estimation and an adaptive neuro-fuzzy unscented Kalman filter (ANFUKF) is used for the feature location’s estimation. In ANFUKF, a neuro-fuzzy inference system (ANFIS) supervises the performance of the unscented Kalman filter (UKF) with the aim of reducing the mismatch between the theoretical and actual covariance of the residual sequences to get better consistency. Findings The simulation and experimental results indicate that the consistency and estimated accuracy of the proposed algorithm are superior FastSLAM. Originality/value The main contribution of this paper is the introduction of MFastSLAM to solve the problems of FastSLAM.

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“…Therefore, these two points are promising advantages as same as the genetic algorithm. If PSO could utilize any local information without complicated calculation or direct calculation of the gradient, efficiency of the algorithm will be better without lack of the previous two advantages of PSO (Maeda & Kuratani, 2006).…”

Section: Introductionmentioning

confidence: 99%

Cauchy mutation based on objective variable of Gaussian particle swarm optimization for parameters selection of SVM

Law

2011

Expert Systems with Applications

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On simultaneous perturbation particle swarm optimization

Cited by 10 publications

References 14 publications

Combination of particle swarm optimization and simultaneous perturbation

Combination of particle swarm optimization and simultaneous perturbation

A mutated FastSLAM using soft computing

Cauchy mutation based on objective variable of Gaussian particle swarm optimization for parameters selection of SVM

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