Artificial Bee Colony Algorithm with an Adaptive Search Manner

Abstract

Artificial bee colony (ABC) can effectively solve some complex optimization problems. However, its convergence speed is slow and its exploitation capacity is insufficient at the last search stage. In order to solve these problems, this paper proposes a modified ABC with an adaptive search manner (called ASMABC). There are two important search manners: exploration and exploitation. A suitable search manner is beneficial for the search. Then, an evaluating indicator is designed to relate the current search status. An explorative search strategy and another exploitative search strategy are selected to build a strategy pool. According to the evaluating indicator, an adaptive method is used to determine which kind of search manner is suitable for the current search. To verify the performance of ASMABC, 22 complex problems are tested. Experiment result shows that ASMABC achieves competitive performance when contrasted with four different ABC variants.