Enhancing artificial bee colony algorithm with depth-first search and direction information

In recent years, Artificial Bee Colony (ABC) algorithm has been criticised for its solution search equation, which makes the search capability bias to exploration at the expense of sacrificing exploitation. To solve the defect, many improved ABC variants have been proposed aiming to utilise the elite individuals. Although these related works have been shown to be effective, they rarely take the factor of search direction into account. In fact, the search direction has an important role in determining the performance of ABC. Thus, in this work, we are motivated to investigate how to combine the idea of utilising the elite individuals with the search direction, and a new ABC variant, called DDABC, is designed. In the DDABC, the Depth-First Search (DFS) mechanism and Direction Information Learning (DIL) mechanism are introduced, and the former mechanism is to allocate more computation resources to the elite individuals, while the latter mechanism aims to adapt the search to the promising directions. To verify the effectiveness of the DDABC, experiments are carried out on 22 classic test functions and three relative ABC variants are included as the competitors. The comparison results show the competitive performance of our approach. Copyright © 2024 Inderscience Enterprises Ltd.