Balanced artificial bee colony algorithm based on multiple selections
Hongyan Shi, Weifeng Zhao, Yu Su · 2017
In view of the shortcomings of the artificial bee colony (ABC) algorithm, such as slow convergence speed, low convergence accuracy and easy to fall into local optimum, a balanced colony algorithm (NABC) based on multiple selections strategy is proposed. Firstly, the fitness and the current optimal honey source position are considered fully to improve the search strategy in onlooker searching stage, which can balance the global search ability and local search ability in different periods; Secondly, a unidimensional exploratory search strategy is used to those which reach the limit search without updating the source, then improve the possibility of jumping out of the local optimal in scout stage. Finally, the simulation results of the six standard test functions show that the NABC algorithm has higher accuracy and faster convergence speed than ABC.