An Improved Artificial Bee Colony Algorithm with Guided Normative Knowledge
Lin Xiao · 2013
An improved artificial bee colony (ABC) algorithm is proposed to solve numerical function optimization problems. Inspired by the double evolutionary space of cultural algorithm, the proposed algorithm takes advantage of the normative knowledge of reliability space to guide the search region and control the radius of the local search space self-adaptively. Thus, the convergence speed and the exploitation ability are enhanced. In order to maintain diversity, a dispersal strategy is designed to balance global exploration and local exploitation of population capacity.Moreover, different approaches are used to explore new positions in various evolutionary stages. The experimental results demonstrate that the proposed algorithm outperforms existing artificial bee colony algorithms on a number of standard test functions both in convergence speed and solution quality.