Group‐guided artificial bee colony algorithm with elastic adjustment strategy

Summary Artificial bee colony algorithm (ABC) is a new intelligent optimization method based on swarm intelligence. It is an algorithm that simulates the social behavior of bees in nature and has a performance of good search in all kinds of optimization problems. Over the past few years, ABC algorithm has become popular because of its stable performance and good structure. ABC algorithm performance, however, is often influenced by both exploitation and exploration abilities, and balancing exploitation and exploration is particularly important. This article proposes a novel variant of ABC, which is called elastic adjusted group guided artificial bee colony algorithm (EGABC). In EGABC, we propose a group guidance strategy with flexible ability to adjust. The main purpose is to effectively use the known information, speed up convergence, and play a balance between search exploitation and exploration ability. To verify the validity of EGABC, we conducted experiments on 13 classic benchmark problems and CEC2013 test suite. Based on experimental results, the EGABC strategy is highly competitive and can improve the ABC algorithm's performance significantly.