The neighborhood of canonical deterministic PSO

2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

et al. 2012

Self Cite

This paper studies the particle swarm optimization (PSO) with switched topology and its application to the multisolution problems (MSPs). The switched topology can give variety in the swarm structure and can be effective to control information transmission speed. We introduce the switched distance as a basic measure to evaluate the switched topology. The proposed PSO does not include stochastic parameters and is suitable for theoretical analysis. Applying the proposed PSO to typical benchmarks of the MSPs, the algorithm efficiency is investigated.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Particle swarm optimization with switched topology and deterministic parameters

Sano

Shindo

2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

et al. 2012

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“…Since the deterministic dynamical systems have strong analysis theory of the stability, several deterministic PSOs have been presented and their stability has been analyzed [10]. We have also proposed the canonical deterministic particle swarm optimization (CD-PSO) where the deterministic parameters are normalized and the dynamics is described by a canonical form equation [11]- [13]. The CD-PSO is convenient to grasp the effects of parameters on stability and dynamics.…”

Section: Introductionmentioning

confidence: 99%

PSO-based multiple optima search systems with switched topology

Sano

Shindo

2012 IEEE Congress on Evolutionary Computation

et al. 2012

Self Cite

This paper discusses a particle swarm optimization (PSO) with switched topology and its application to the multisolution problems. First, we introduce a deterministic PSO characterized by normalized deterministic parameters and a canonical form system equation. This system is convenient to grasp effects of parameters on the stability. Second, we investigate effects of the average distance of several the swarm topologies on the search capability. Especially, we introduce the switched topology where any information is not transmitted from the edge if the switch is off. Third, we consider an application to exploring multiple periodic points in simple dynamical systems. Performing numerical experiments for typical examples, the algorithm performance is investigated.

“…The information translation structure of particles can be regarded as a network structure. Several researchers have studied the relatioship between the search performance and the network structure [17] [18]. A ring structure is effective for searching for the optimum value of a multimodal function.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…Comparing these structures, the full connection exhibits quicker convergence. On the other hand, the ring structure exhibits an extraordinary search performance for a multimodal function [17][18].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

A Nonlinear Blind Source Separation System Using Particle Swarm Optimization Algorithm

Kurihara

Journal of Signal Processing

2013

Blind source separation (BSS) is a technique for recovering original source signals from mixed signals without the aid on information of the source signals. The system restores the original source signals using the probability of the distribution of the original signal. In this paper, we consider the case where the original source signals are nonlinearly mixed. In general, the separation of the nonlinear mixed signals is difficult. In order to solve this problem, we apply a radial basis function (RBF) network with the nonlinear BSS system. The RBF network can approximate the nonlinear mapping. Therefore, the inverse mapping of the nonlinear mixture system is approximated by the RBF network. For the system to approximate the inverse mapping, it is necessary to adjust the parameters of the RBF network. We assume the original source signals to be independent of each other. In this case, if the mixed signals can be separated, the higher-order cross-moment of the output signals is decreased. In order to adjust the parameters of the RBF network, particle swarm optimization is used. We confirm the separation performance by numerical simulations. Simulation results indicate that the proposed approach has good performance.