摘要：AbstractThe traditional artificial bee colony algorithm has the disadvantages of insufficient population diversity, strong equation-searching ability but weak developing capacity, which leads to poor quality of solution, local optimum and slow global convergence. This paper increases the population diversity by unlearning initialization, improves the quality of the solution, as well as avoids the local optimum. What’s more, we introduce the cross-operation and the global optimal value into the search process so that it can generate candidate solution next to the global optimal. Thus, it accelerates global convergence speed. The simulation results show that the optimization performance of different optimal function algorithm is better when the cross-factor is about 0.5. An improved ABC algorithm based on initial population and neighborhood search results show that the optimization accuracy is improved by about 2 times, which avoids the local optimum generally. Meanwhile, the number of iteration decreases about 8% to 15%, accelerating the global convergence speed.
