Abstract: In order to solve multi-objective continuous optimization problem,this paper gave the solving process accor-ding to artificial bee colony algorithm theory,and pointed out that the updating strategy in the algorithm has defect of blind searching and missing good individuals,thus proposed an improved strategy．The improved strategy has two parts．First,a self-adapting searching operator is designed to enable the algorithm to adjust the searching range automa-tically according to individual quality during the iterative process,leading to a more accurate and efficient algorithm searching process.Second,the newly produced individuals are recorded by external archive,and external archive is combined to reconstruct the colony after a iteration,which can save good individuals in the iterative process．The experiment compares improved artificial bee colony algorithm with NSGA2algorithm,artificial bee colony algorithm and superior algorithm alike in papers．The comparison result indicates the improved artificial bee colony algorithm has good convergence and uniformity in solving multi-objective continuous optimization problem．

Key words: Artificial bee colony algorithm,Multi-objective continuous optimization problem,Updating strategy,Self-adapting searching operator