Neural Computing and Applications

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

Characterization of particle swarm optimization with diversive curiosity

ISNN 2008


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.


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



This research was supported by a COE program (#J19) granted to Kyushu Institute of Technology by the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. It was also supported by Grant-in-Aid Scientific Research(C)(18500175) from MEXT, Japan.


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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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