A variant of particle swarm optimizer(PSO) based on dynamic neighborhood topology and mutation
factor(DNMPSO) is proposed in this paper. In DNMPSO, the neighborhood of a particle are not fixed but dynamically
changed, and the learning mechanism of a particle includes two parts，the historical best experience of the particle
itself，and the experiences of its all neighbors. To effectively solve multimodal problems, the parallel hybrid mutation is used
to work for local search, which improves the ability of escaping from local optima. The results demonstrate good performance
of the DNMPSO algorithm in solving complicated multimodal problems when compared with other PSO algorithms.