A consolidated model of Particle Swarm Optimisation variants

Pace, Shannon; Cain, Andrew; Woodward, Clinton J.

doi:10.1109/cec.2012.6252967

Cited by 2 publications

(3 citation statements)

References 30 publications

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“…In order to establish a common understanding from the implementation variant of meta-heuristics for the purpose of consistency and repeatability experiment [12], many researchers have introduced different implementation frameworks, models or taxonomies. In this research, the frameworks are defined as an abstraction of techniques that are derived based on the proposed taxonomy.…”

Section: Meta-heuristics Implementation Frameworkmentioning

confidence: 99%

The implementation frameworks of meta-heuristics hybridization with dynamic parameterization

Masrom

Rahman

Abidin

et al. 2018

J. Fundam and Appl Sci.

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The hybridization of meta-heuristics algorithms has achieved a remarkable improvement from the adaptation of dynamic parameterization. This paper proposes a variety of implementation frameworks for the hybridization of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) and the dynamic parameterization. In this paper, taxonomy of the PSO-GA with dynamic parameterization is presented to provide a common terminology and classification mechanisms. Based on the taxonomy, thirty implementation frameworks are possible to be adapted. Furthermore, different algorithms that used the implementation frameworks with sequential scheme and dynamic parameterizations approaches are tested in solving a facility layout problem.

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Section: Meta-heuristics Implementation Frameworkmentioning

confidence: 99%

The implementation frameworks of meta-heuristics hybridization with dynamic parameterization

Masrom

Rahman

Abidin

et al. 2018

J. Fundam and Appl Sci.

View full text Add to dashboard Cite

show abstract

“…After all particles moved, their velocities are updated. The new velocity comprises two distinct components: a cognitive and a social component [11]. The cognitive component is the position p c,i , with the best target value a particle i has seen in the past.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…A particle swarm that updates velocities dimension by dimension is biased towards movement along axis parallels, even when the problem is rotationally symmetric. As a direct result, new PSO versions (like SPSO 2011 [11]) were developed to overcome the bias.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Improved estimation of hand postures using depth images

Hamester¹,

Jirak²,

Wermter³

2013

2013 16th International Conference on Advanced Robotics (ICAR)

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Abstract-Hand pose estimation is the task of deriving a hand's articulation from sensory input, here depth images in particular. A novel approach states pose estimation as an optimization problem: a high-dimensional hypothesis space is constructed from a hand model, in which particle swarms search for the best pose hypothesis. We propose various additions to this approach. Our extended hand model includes anatomical constraints of hand motion by applying principal component analysis (PCA). This allows us to treat pose estimation as a problem with variable dimensionality. The most important benefit becomes visible once our PCA-enhanced model is combined with biased particle swarms. Several experiments show that accuracy and performance of pose estimation improve significantly.

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A consolidated model of Particle Swarm Optimisation variants

Cited by 2 publications

References 30 publications

The implementation frameworks of meta-heuristics hybridization with dynamic parameterization

The implementation frameworks of meta-heuristics hybridization with dynamic parameterization

Improved estimation of hand postures using depth images

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