Neural Computing and Applications

, Volume 18, Issue 5, pp 409–415 | Cite as

Characterization of particle swarm optimization with diversive curiosity

ISNN 2008

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.

Keywords

Evolutionary particle swarm optimization Temporally cumulative fitness function Diversive curiosity Premature convergence Exploitation Exploration 

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Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  1. 1.Department of Brain Science and Engineering, Graduate School of Life Science and Systems EngineeringKyushu Institute of TechnologyKitakyushuJapan

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