Quantum particle swarm optimization algorithm based on diversity migration strategy

Particle swarm optimization algorithm has been successfully applied to solve practical optimization problems due to its simplicity and efficiency. However, the traditional particle swarm optimization algorithm has inferior search performance in complicated high-dimensional optimization issues and is prone to falling into local optima. To address these problems, a new migration mechanism is introduced and a quantum particle swarm optimization method based on diversity migration is proposed. The strategy can capture different ranges of particles in the population, and the selection of migrating individuals depends not only on their fitness values but is also influenced by the positions within the population. The individual with the minimal average Hamming distance in the population can indicate the direction of iterative population optimization. After comparing the fitness values and the average Hamming distance between particles, the particles deviating from the central range of the population are replaced. The performance of the proposed algorithm is investigated under seven different sets of benchmark function optimization problems in the CEC2020 single-objective boundary-constrained optimization competition, and is compared with those of several other representative optimization algorithms. The quantum particle swarm optimization algorithm based on diversity migration strategy outperforms other typical optimization algorithms. Moreover, the proposed algorithm is convergent and stable.