An Elitist Promotion Quantum-Behaved Particle Swarm Optimization Algorithm

An elitist promotion quantum-behaved particle swarm optimization (EP-QPSO) based on differential evolution operators is proposed and studied empirically in this paper. The proposed EP-QPSO uses the differential evolution operators in the elitist promotion procedure to perform search on each particle's personal best of the swarm individually to enhance the global search ability of the algorithm. A comprehensive simulation study is conducted on the unimodal and multimodal benchmark functions. Comparing with the original differential evolution algorithm and three state-of-the-art quantum-behaved particle swarm optimization algorithms, the simulation results indicate that proposed EP-QPSO has better global search capability and faster convergence speed.