A novel chaotic artificial bee colony algorithm based on Tent map

A novel self-adaptive chaotic artificial bee colony algorithm based on Tent map (STOC-ABC) is proposed to enhance the global convergence and the population diversity. In the STOC-ABC, Tent chaotic opposition-based learning initialization method is presented to diversify the initial individuals and obtain good initial solutions. Furthermore, the self-adaptive Tent chaotic searching is implemented at the zones nearby individual optimum solution to help the artificial bee colony (ABC) algorithm to escape from the local optimum effectively. Moreover, the tournament selection strategy in onlooker bee phase is employed to increase the ability of the algorithm and avoid premature convergence. Experiments on six complex benchmark functions with high-dimension, the results further demonstrate that, the STOC-ABC not only accelerates the convergence rate and improves solution precision, but also provides excellent performance in dealing with complex high-dimensional functions.