Application of An Improved Artificial Bee Colony Algorithm

Abstract

An improved artificial bee colony algorithm which overcome the shortcomings, including slow convergence speed, easily falling into local optimum value, Mechanism of other bionic intelligent optimization algorithms is put forward. The algorithm with number theory of uniform distribution of good point set, first established algorithm initialization model, in order to maintain the diversity of the population; Then, the location is updated iteratively to maintain the diversity of the population and prevent the algorithm from falling into the local optimum. Finally, the seven standard test functions are selected to carry out the experiment and simulation. The results show that compared with the ABC algorithm, GABC algorithm and so on. The algorithm (GCABC) the convergence speed and accuracy have been improved significantly, and to improve the overall searching ability, effectively avoid the population into a local optimum, to solve the problem of multi-modal optimization.