An Improved QPSO Algorithm Based on Entire Search History

Abstract

An improved QPSO algorithm based on entire search history (ESH-QPSO) is proposed. ESH-QPSO is an integration of the entire search history scheme and a standard quantum-behaved particle swarm optimization (QPSO). It guarantees that all updated positions are not revisited before, which helps prevent premature convergence. The entire search history scheme partitions the continuous search space into sub-regions by using BSP tree. The partitioned sub-region servers as mutation range such that the corresponding mutation is adaptive and parameter-less. When sub-regions are formulated as which certain overlap exists between adjacent sub-regions, this allows particle move from a sub-region to another with better fitness. Compared with other traditional algorithms, the experiment results on 8 standard testing functions show that the proposed algorithm is superior regarding the optimization of multimodal and unimodal functions, with enhancement in both convergence speed and precision those demonstrate the effectiveness of the algorithm.