Abstract: Employed bee foraging strategies have a greater impact on the performance of artificial bee colony algorithm. The single foraging strategy is difficult to apply to all the search space of the problems. And the different stages of the algorithm performs differently by using different employed bee foraging strategies.How to choose the best foraging strategy is very important for the given function optimization problem. To solve this problem， a strategy self-adaptive selection colony algorithm was presented， based on feedback. The optimal foraging strategy could be automatically selected for the given problem during the optimization process using the praposed algorithm. Experimental results showed that compared with the ABC (artificial bee colony algorithm)， the PSO (particle swarm optimization algorithm)， the DE (differential evolution algorithm)， and the GA (genetic algorithm)， the optimization capability of the SSABC algorithm has been improved greatly.

Key words: self-adaptive, artificial bee colony algorithm, feedback, function optimization, intelligence algorithm