A novel intelligent particle optimizer for global optimization of multimodal functions
Zhen Ping Ji, Huilian Liao, Yiwei Wang, Qinghua Wu · 2007
A novel intelligent particle optimizer based on subvectors (IPO) is proposed in this paper, which is inspired by conventional particle swarm optimization (PSO). IPO uses only one particle instead of a particle swarm. The position vector of this particle is partitioned into a certain number of subvectors, and the updating process is based on subvectors and evolved to subvectors updating process, in which the particle adjusts the velocity intelligently by introducing a new learning factor. This learning factor utilizes the information contained in the previous updating process. The particle is capable of increasing its velocity towards the global optimum in lower dimensional subspaces and not being trapped in local optima. Experimental results have demonstrated that IPO has impressive ability to find global optimum. IPO performs better than recently developed PSO-based algorithms in solving some complicated multimodal functions.