Dynamic Inertia Weight in Particle Swarm Optimization

Borowska, Bożena

doi:10.1007/978-3-319-70581-1_6

Cited by 2 publications

(1 citation statement)

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“…Calculate the inertia value as ω = ω max − iter N g ω max − ω min , where common values for these parameters are ω min = 0.4 and ω max = 0.9. Many inertia calculations have appeared in the relevant literature such as constant inertia [58], linearly decreasing inertia [59], exponential inertia [60], random inertia calculation [61], dynamic inertia [62], fuzzy inertia calculation [63], etc. The present method of calculating the inertia was chosen because it decreases linearly with time, and for large values of the inertia, it allows a wider search in the search space, while for low values, it allows a more focused search.…”

Section: (E)mentioning

confidence: 99%

Locating the Parameters of RBF Networks Using a Hybrid Particle Swarm Optimization Method

Tsoulos

Charilogis

2023

Algorithms

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In the present work, an innovative two-phase method is presented for parameter tuning in radial basis function artificial neural networks. These kinds of machine learning models find application in many scientific fields in classification problems or in function regression. In the first phase, a technique based on particle swarm optimization is performed to locate a promising interval of values for the network parameters. Particle swarm optimization was used as it is a highly reliable method for global optimization problems, and in addition, it is one of the fastest and most-flexible techniques of its class. In the second phase, the network was trained within the optimal interval using a global optimization technique such as a genetic algorithm. Furthermore, in order to speed up the training of the network and due to the use of a two-stage method, parallel programming techniques were utilized. The new method was applied to a number of famous classification and regression datasets, and the results were more than promising.

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