Numerical function optimization of improved artificial bee colony algorithm and its application in finite element model modification

Abstract An improved artificial bee colony algorithm is proposed in this paper to improve the problems of strong exploration ability, weak exploitation ability and being easy to fall into local optimum of the basic artificial bee colony algorithm. The quality of the initial solution is improved by adopting the principle of equally spaced random distribution in the initialization phase. In the stage of employed bee and onlooker bee, it introduces the two-way search strategy and the adaptive decision factor γ, which also changes the search step length at the same time to balance algorithm exploration and exploitation capabilities by replacing the original search strategy. fifteen benchmark test functions were used to verify the optimal search ability of the improved artificial bee colony algorithm, and the improved algorithm was successfully applied to the finite element model modification. The results show that the improved algorithm can effectively improve and balance the exploitation and exploration ability of the algorithm, and show excellent global search ability. At the same time, the improved algorithm has higher correction efficiency and better applicability and practicability in the finite element model modification.