Chicken swarm optimization algorithm based on quantum behavior and its convergence analysis

Aiming at the defects of chicken swarm optimization algorithm, such as easy to fall into local optimal, premature convergence and slow convergence, a chicken swarm optimization algorithm based on quantum behavior is proposed in this paper. A quantized potential well model is established based on the individual information of chicken swarm. According to the existing individual extremum and global extremum obtained by the original updating formula, Monte Carlo random sampling is adopted to complete the updating of individual extremum, and the search is conducted at a parallel Angle near individual extremum and global extremum, which improves the local search performance of the algorithm. At the same time, the convergence of quantum-behavior chicken swarm optimization algorithm is discussed in this paper, and QCSO is proved to be a globally convergent optimization algorithm. The optimization capability of QCSO is tested by using basic test function, and the results show that the optimization performance of this algorithm is greatly improved compared with the original algorithm.