Improved Artificial Bee Colony Algorithms for Function Optimization
Jing Liang · 2013
In order to enhance the performance of artificial bee colony algorithm in solving complex function optimization problems,this paper analysed the shortcoming of escape behavior of scout bees,and improved it.The improved algorithm defines escape index,making it precisely reflecting the effect of individual status on the premature convergence of algorithm,redesigns the selection scheme,making scout bees choosing individual escape operation that might result in algorithm premature convergence adaptively,improves the escape operator,reducing the blindness of escape operation.Nine typical experiments prove that the improved algorithm could converge efficiently under assignment convergence accuracy,and the improved algorithm could converge with more convergence accuracy and speed compared with basic artificial colony algorithm and existing typical improved versions,thus proves the improved strategy proposed in this paper could boost capability of solving complex function optimization problems.