Enhancing artificial bee colony algorithm with multi-elite guidance

Artificial bee colony (ABC) algorithm is a relatively new paradigm of swarm intelligence based optimization technique, which has attracted a lot of attention for its simple structure and good performance. For some complex optimization problems, however, the performance of ABC is challenged due to its solution search equation that has strong explorative ability but poor exploitative ability. To solve this defect, in this work, we propose an improved ABC algorithm by using multi-elite guidance, which has the benefits of utilizing valuable information from elite individuals to guide search while without losing population diversity. First, we construct an elite group by selecting some elite individuals, and then introduce two improved solution search equations into the employed bee phase and onlooker bee phase based on the elite group, respectively. Last, we develop a modified neighborhood search operator by utilizing the elite group as well, which aims to achieve a better tradeoff between explorative and exploitative abilities. To verify our approach, 50 well-known test functions and one real-world optimization problem are used in the experiments, including 22 scalable basic test functions and 28 complex CEC2013 test functions. Seven different well-established ABC variants are involved in the comparison and the results show that our approach can achieve better or at least comparable performance on most of the test functions.