Analysis of convergence property of PSO and its application to nonlinear blind source separation

Kurihara, Takayuki; Jin’no, Kenya

doi:10.1109/cec.2013.6557673

Cited by 11 publications

(11 citation statements)

References 29 publications

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“…They provided a new definition for the convergence rate of a stochastic algorithm as well as definitions for three types of convergence according to the correlations between the convergence rate and the objective function values. Kurihara and Jin'no [204] analyzed the convergence property of the PSO and its application to the nonlinear blind source separation system. The interparticle communication of the PSO was realized by the past history of the neighbors and depended on the network structure of the swarm.…”

Section: Convergence Analysismentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

688

380

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Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

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Section: Convergence Analysismentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

688

380

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“…However, while the ACO's best information propagation method is the ring topology [12], the PSO's best communication topology is the Von Neumann architecture [13], while the ABO employs the star topology which connects all the buffalos together since the entire herd works as a unit in search of solutions for its information propagation [14]. A diagrammatic representation of the different communication topologies are presented in Figure 3 below.…”

Section: Communication Issues In Abo Convergencementioning

confidence: 99%

Convergence Analysis of the African Buffalo Optimization Algorithm

Odili¹,

Kahar

Noraziah³

2016

International Journal of Simulation: Systems, Science &Amp; Technology

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This paper presents the convergence analysis of the newly-developed African Buffalo Optimization algorithm. African Buffalo Optimization is a simulation of the organizational skills of the African buffalos using two basic sounds: /waaa/ and /maaa/ as they transverse the African landscape in search of grazing pastures. The African Buffalo Optimization has proven to be quite successful since its development hence the need to examine its convergence behaviour. The analysis of the convergence of Natureinspired optimization algorithms is necessary to help researchers and practitioners understand the workings of the algorithms in the algorithms' attempts at solutions. After a number of evaluations, this study discovered that the convergence of African Buffalo Optimization is a function of the population size, communication topology, parameter-set, landscape topology and the objective function being optimized.

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“…The problem of multichannel signal separation has found many applications in signal processing and modern communications [1]- [6]. Typical applications include separating competing speakers using multiple microphone measurements, noise canceling, channel equalization, biomedical signal analysis, image processing and many others.…”

Section: Introductionmentioning

confidence: 99%

Adaptive multichannel LMS signal decoupling

Haweel

2015

2015 International Conference on Communications, Signal Processing, and Their Applications (ICCSPA'15)

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Multichannel signal separation is a problem frequently encountered in many signal processing and communications applications. A simple and efficient sequential LMS adaptive multichannel signal decoupling algorithm is presented. It is based on minimizing the second commulant for the received mixed signals, although it can also be applied to minimize higher order commulants. The algorithm employs instantaneous signal cross products as estimates for the true cross-correlation based on ensemble averaging. No time averaging is required as in the case of most existing methods. The power of the decoupled output signals may be adjusted to the required levels. The proposed algorithm is fast, computationally efficient and suitable for real time applications. Analysis and simulations are provided to justify the efficiency of the proposed algorithm.

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Analysis of convergence property of PSO and its application to nonlinear blind source separation

Cited by 11 publications

References 29 publications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Convergence Analysis of the African Buffalo Optimization Algorithm

Adaptive multichannel LMS signal decoupling

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