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Neural Computing and Applications

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01.03.2012 | Swam Intelligence

An inertia-adaptive particle swarm system with particle mobility factor for improved global optimization

verfasst von: Sayan Ghosh, Swagatam Das, Debarati Kundu, Kaushik Suresh, B. K. Panigrahi, Zhihua Cui

Erschienen in: Neural Computing and Applications | Ausgabe 2/2012

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Abstract

Particle Swarm Optimization (PSO) has recently emerged as a nature-inspired algorithm for real parameter optimization. This article describes a method for improving the final accuracy and the convergence speed of PSO by firstly adding a new coefficient (called mobility factor) to the position updating equation and secondly modulating the inertia weight according to the distance between a particle and the globally best position found so far. The two-fold modification tries to balance between the explorative and exploitative tendencies of the swarm with an objective of achieving better search performance. We also mathematically analyze the effect of the modifications on the dynamics of the PSO algorithm. The new algorithm has been shown to be statistically significantly better than the basic PSO and four of its state-of-the-art variants on a twelve-function test-suite in terms of speed, accuracy, and robustness.

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Titel: An inertia-adaptive particle swarm system with particle mobility factor for improved global optimization
verfasst von: Sayan Ghosh
Swagatam Das
Debarati Kundu
Kaushik Suresh
B. K. Panigrahi
Zhihua Cui
Publikationsdatum: 01.03.2012
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 2/2012
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-010-0356-x

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