Incremental attribute based particle swarm optimization

An incremental-attribute based particle swarm optimization (IAPSO) which utilizes incremental learning strategy in function optimization is presented in this paper. Traditionally, particle swarm optimization (PSO) searches all the dimensions at the same time. Decomposition strategy is utilized in IAPSO to decompose the whole search space (D-dimension) into D numbers of one-dimensional space. In this approach, incremental learning strategy optimizes the function by searching the D-dimensional space one by one. Experimental results show that IAPSO gets more accurate and stable results than standard PSO in multimodal problems. IAPSO could avoid the “local optima”, i.e., it has better “exploration” ability than standard PSO.