A quantum-behaved particle swarm optimization algorithm with self-adaptive elitist crossover

Abstract

A quantum-behaved particle swarm optimization with self-adaptive elitist crossover (EXQPSO) is proposed in this paper. In the proposed EXQPSO algorithm, the crossover operator is performed on the elitist individuals to improve the qualities of the search individuals. Moreover, a self-adaptive scheme is proposed to automatically tune the crossover probability according to the performance of the crossover operator. Experiments on the well-known benchmark test suite showed that the proposed EXQPSO is better than the original QPSO in terms of global search capability and computation efficiency.