Characterization of particle swarm optimization with diversive curiosity

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.