Multimodal Function Optimization Based on Improved ABC Algorithm
Ryan Wen Liu, Libo Liu, Tuqian Zhang, Jing Liu · 2016
In order to improve the searching speed and the quality of global optimal solution, we propose an improved algorithm based on Artificial Bee Colony(ABC) algorithm, which can search the space by stochastic optimization and dynamic regulation (named SRABC). Firstly, the improved algorithm can update the next location of ABC algorithm, which can perfect the correlation for the bee colony. Secondly, we used the dynamic regulation to search for fitness function by restraining the direction, the approach can improve local search ability effectively. By testing 5 multimodal functions, the simulation results show that the SRABC algorithm has significant improvements in convergence rate and precision rate of global optimal solution.