To the problems of premature convergence frequently appeared in standard particle swarm optimization(PSO)
algorithm and its poor convergence, a particle swarm optimization algorithm with the search operator of artificial bee colony
is proposed. Firstly, the method makes full use of the exploration of artificial bee colony algorithm search operator to help
the algorithm to jump out of the likely local optima. Then to enhance the global convergence, when producing the initial
population, both chaotic maps and opposition-based learning based method is proposed. Moreover, simulation experiment
on a suite of 12 benchmark functions is given, and the comparisons with other algorithms are provided. The results show that
the proposed approach has better convergence rate and great capability of preventing premature convergence.