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Neural Computing and Applications

Erschienen in:

01.06.2009 | ISNN 2008

Characterization of particle swarm optimization with diversive curiosity

verfasst von: Hong Zhang, Masumi Ishikawa

Erschienen in: Neural Computing and Applications | Ausgabe 5/2009

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Abstract

For obtaining superior search performance in particle swarm optimization (PSO), we proposed particle swarm optimization with diversive curiosity (PSO/DC). The mechanism of diversive curiosity in PSO can prevent premature convergence and ensure exploration. To clarify the characteristics of PSO/DC, we estimated the range for appropriate parameter values, and investigated the trade-off between exploration and exploitation. Applications of the proposed method to a two-dimensional multimodal optimization problem and a suite of five-dimensional benchmark problems well demonstrate its effectiveness. Our experimental results basically accord with the findings in psychology, i.e., diversive curiosity being prone to exploration and anxiety.

Vorheriger Artikel Editorial to special issue: computational intelligence for optimization, modeling and control

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Computing environment: Intel(R) Xeon(TM); CPU 3.40GHz; Memory 2.00GB RAM; Computing tool: Mathematica 5.2; Computing time: about 3 min.

Success means that the fitness value of the best particle is over 0.3950.

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Zhang H, Ishikawa M (2008) Characteristics of particle swarm optimization with diversive curiosity. In: Fifth international symposium on neural networks (ISNN2008). Beijing, China, p 10

Titel: Characterization of particle swarm optimization with diversive curiosity
verfasst von: Hong Zhang
Masumi Ishikawa
Publikationsdatum: 01.06.2009
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 5/2009
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-009-0252-4

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