Optimal parameter regions for particle swarm optimization algorithms

Harrison, Kyle Robert; Ombuki-Berman, Beatrice M.; Engelbrecht, Andries P.

doi:10.1109/cec.2017.7969333

Cited by 20 publications

(14 citation statements)

References 29 publications

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“…However, the topology must be considered as a parameter to be tuned given that the best topology to employ is dependent upon both the optimization problem and computational budget [33,34]. Furthermore, it was shown by Harrison et al [29] that the topology employed has a noticeable influence on the regions in parameter space that lead to good performance.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…There has been a number of studies that have empirically examined the performance of various PSO parameter configurations [6,8,9,10,28,29]. However, there is no general consensus as to which parameter configurations lead to the best performance.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, Harrison et al [29] empirically investigated 1012 parameter configurations over a set of 22 benchmark functions with the primary objective of determining which areas of parameter space lead to good performance of the PSO algorithm. Both of these studies have led to an enhanced understanding of the general region in parameter space where good parameter configurations lie.…”

Section: Introductionmentioning

confidence: 99%

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Optimal parameter regions and the time-dependence of control parameter values for the particle swarm optimization algorithm

Harrison

Engelbrecht

Ombuki-Berman

2018

Swarm and Evolutionary Computation

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The particle swarm optimization (PSO) algorithm is a stochastic search technique based on the social dynamics of a flock of birds. It has been established that the performance of the PSO algorithm is sensitive to the values assigned to its control parameters. Many studies have examined the long-term behaviours of various PSO parameter configurations, but have failed to provide a quantitative analysis across a variety of benchmark problems. Furthermore, two important questions have remained unanswered. Specifically, the effects of the balance between the values of the acceleration coefficients on the optimal parameter regions, and whether the optimal parameters to employ are timedependent, warrant further investigation. This study addresses both questions

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal parameter regions and the time-dependence of control parameter values for the particle swarm optimization algorithm

Harrison

Engelbrecht

Ombuki-Berman

2018

Swarm and Evolutionary Computation

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“…However, as previous works have identified (van Zyl and Engelbrecht, 2014;Harrison et al, 2016a,b), this is not always the case. Recently, evidence has been provided to suggest that parameter configurations which adhere to a well-known convergence criterion will generally lead to better performance than parameter configurations that violate the criterion (Cleghorn and Engelbrecht, 2016;Harrison et al, 2017). Furthermore, it has been shown that many of the parameter configurations which violate the convergence criterion lead to worse performance than random search (Cleghorn and Engelbrecht, 2016).…”

Section: Optimizermentioning

confidence: 99%

“…(3) will generally lead to better performance than parameter configurations which violate the criterion (Cleghorn and Engelbrecht, 2016;Harrison et al, 2017). Specifically, it was shown that a majority of theoretically unstable parameter configurations cause the PSO algorithm to perform worse than random search and that selecting theoretically convergent parameters drastically increases the likelihood of PSO outperforming random search (Cleghorn and Engelbrecht, 2016).…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Self-adaptive particle swarm optimization: a review and analysis of convergence

2017

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Particle swarm optimization (PSO) is a population-based, stochastic search algorithm inspired by the flocking behaviour of birds. The PSO algorithm has been shown to be rather sensitive to its control parameters and thus performance may be greatly improved by employing appropriately tuned parameters. However, parameter tuning is typically a time-intensive empirical process. Furthermore, a priori parameter tuning makes the implicit assumption that the optimal parameters of the PSO algorithm are not timedependent. To address these issues, self-adaptive particle swarm optimization (SAPSO) algorithms adapt their control parameters throughout execution. While there is a wide variety of such SAPSO algorithms in the literature, their behaviours are not well understood. Specifically, it is unknown whether these SAPSO algorithms will even exhibit convergent behaviour. This paper addresses this lack of understanding by investigating the convergence behaviours of 18 SAPSO algorithms both analytically and empirically. This paper also empirically examines whether the adapted parameters reach a stable point and whether the final parameter values adhere to a well-known convergence criterion. The results depict a grim state for SAPSO algorithms; over half of the SAPSO algorithms exhibit divergent behaviour while many others prematurely converge.

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