A novel quantum evolutionary algorithm based on dynamic neighborhood topology

Abstract

A variant of quantum evolutionary algorithm based on dynamic neighborhood topology(DNTQEA) is proposed in this paper. In DNTQEA, the neighborhood of a quantum particle are not fixed but dynamically changed, and the learning mechanism of a quantum particle includes two parts, the global best experience of all quantum particles in population, and the best experiences of its all neighbors, which collectively guide the evolving direction. The experimental results demonstrate the better performance of the DNTQEA in solving combinatorial optimization problems when compared with other quantum evolutionary algorithms.