Defining a Standard for Particle Swarm Optimization

Bratton, Daniel J.; Kennedy, James

doi:10.1109/sis.2007.368035

Cited by 1,044 publications

(703 citation statements)

References 11 publications

(14 reference statements)

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“…Four hundred Monte Carlo realizations are conducted in the experiments and in each realization, discrete true parameter values are randomly perturbed in the very vicinity of themselves. Moreover, for both of the experiments, the same PSO settings, such as swarm size, update rules, swarm topology and swarm initialization, are chosen based on recommendations in the literature and empirical simulations [35]. We observed that fine tuning the parameters would not provide significant improvements.…”

Section: Simulation Resultsmentioning

confidence: 99%

Multipath channel identification by using global optimization in ambiguity function domain

Güldoǧan

Arıkan

2011

Signal Processing

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a b s t r a c tA new transform domain array signal processing technique is proposed for identification of multipath communication channels. The received array element outputs are transformed to delay-Doppler domain by using the cross-ambiguity function (CAF) for efficient exploitation of the delay-Doppler diversity of the multipath components. Clusters of multipath components can be identified by using a simple amplitude thresholding in the delay-Doppler domain. Particle swarm optimization (PSO) can be used to identify parameters of the multipath components in each cluster. The performance of the proposed PSO-CAF technique is compared with the space alternating generalized expectation maximization (SAGE) technique and with a recently proposed PSO based technique at various SNR levels. Simulation results clearly quantify the superior performance of the PSO-CAF technique over the alternative techniques at all practically significant SNR levels.

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Section: Simulation Resultsmentioning

confidence: 99%

Multipath channel identification by using global optimization in ambiguity function domain

Güldoǧan

Arıkan

2011

Signal Processing

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“…An alternative is a local topology (e.g. Bratton and Kennedy [3]), where each particle obtains information from only a small number of other particles. For example, the Standard PSO 2007[1] algorithm randomly selects a small number of "informants" for each particle from which the best point is obtained.…”

Section: Multi-objective Particle Swarm Optimizationmentioning

confidence: 99%

“…Koziel and Michalewicz [10] classifies constraint handling techniques for evolutionary algorithms as: (1) techniques that preserve feasibility, (2) techniques based on penalty functions, (3) techniques making a clear distinction between feasible and infeasible solutions and (4) other hybrid techniques. More recently Sienz and Innocente [16] classifies constraint handling strategies for particle swarm optimization as: (1) strategies that reject infeasible solutions (also known as a death penalty approach), (2) strategies that penalize infeasible solutions (also known as a penalty function approach), (3) strategies that preserve feasibility, (4) strategies that cut-off at the boundary, (5) strategies based on a bi-section approach and (6) strategies that repair infeasible solutions. Of these approaches, one of the most popular is to make use of a penalty function approach, where the objective function is penalized for any constraint violation.…”

Section: Introductionmentioning

confidence: 99%

Constrained particle swarm optimization using a bi-objective formulation

Venter

Haftka

2009

Struct Multidisc Optim

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This paper introduces an approach for dealing with constraints when using particle swarm optimization. The constrained, single objective optimization problem is converted into an unconstrained, bi-objective optimization problem that is solved using a multi-objective implementation of the particle swarm optimization algorithm. A specialized bi-objective particle swarm optimization algorithm is presented and an engineering example problem is used to illustrate the performance of the algorithm. An additional set of 13 test problems from the literature is used to further validate the performance of the newly proposed algorithm. For the example problems considered here, the proposed algorithm produced promising results, indicating that it is an approach that deserves further consideration. The newly proposed algorithm provides performance similar to that of a tuned penalty function approach, without having to tune any penalty parameters.

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“…The PSO algorithm was inspired by the social behavior of biological organisms, specifically the ability of groups of some species of animals to work as a whole in locating desirable positions in a given area, e.g. birds flocking to a food source [31,32]. Generally in the PSO method, particles move through the search space using a combination of an attraction to the best solution that they individually have found, and an attraction to the best solution that any particle in their neighborhood has found [31,32].…”

Section: Introductionmentioning

confidence: 99%

“…birds flocking to a food source [31,32]. Generally in the PSO method, particles move through the search space using a combination of an attraction to the best solution that they individually have found, and an attraction to the best solution that any particle in their neighborhood has found [31,32]. Thus, the PSO allows to search the optimum of a nonlinear problem.…”

Section: Introductionmentioning

confidence: 99%

Modelling influenza A(H1N1) 2009 epidemics using a random network in a distributed computing environment

et al. 2015

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In this paper we propose the use of a random network model for simulating and understanding the epidemics of influenza A(H1N1). The proposed model is used to simulate the transmission process of influenza A(H1N1) in a community region of Venezuela using distributed computing in order to accomplish many realizations of the underlying random process. These large scale epidemic simulations have recently become an important application of high-performance computing. The network model proposed performs better than the traditional epidemic model based on ordinary differential equations since it adjusts better to the irregularity of the real world data. In addition, the network model allows the consideration of many possibilities regarding the spread of influenza at the population level. The results presented here show how well the SEIR model fits the data for the AH1N1 time series despite the irregularity of the data and returns parameter values that are in good agreement with the medical data regarding AH1N1 influenza virus. This versatile network model approach may be applied to the simulation of the transmission dynamics of several epidemics in human networks. In addition, the simulation can provide useful information for the understanding, prediction and control of the transmission of influenza A(H1N1) epidemics.

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Defining a Standard for Particle Swarm Optimization

Cited by 1,044 publications

References 11 publications

Multipath channel identification by using global optimization in ambiguity function domain

Multipath channel identification by using global optimization in ambiguity function domain

Constrained particle swarm optimization using a bi-objective formulation

Modelling influenza A(H1N1) 2009 epidemics using a random network in a distributed computing environment

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