Display Omitted Proposed approach is inspired from a technical analyze tool named Bollinger Bands.The weakest point of ABC is, it is one parameter update strategy in each fitness evaluation.Proposed approach is a new additional update equation for all ABC based optimization algorithms.The results are high quality for numerical benchmark functions in solution quality and convergence speed. In this study, a new algorithm that will improve the performance and the solution quality of the ABC (artificial bee colony) algorithm, a swarm intelligence based optimization algorithm is proposed. ABC updates one parameter of the individuals before the fitness evaluation. Bollinger bands is a powerful statistical indicator which is used to predict future stock price trends. By the proposed method an additional update equation for all ABC-based optimization algorithms is developed to speed up the convergence utilizing the statistical power of the Bollinger bands. The proposed algorithm was tested against classical ABC algorithm and recent ABC variants. The results of the proposed method show better performance in comparison with ABC-based algorithm with one parameter update in convergence speed and solution quality.