Artificial bee colony algorithm based on online fitness landscape analysis

Abstract

As an effective optimization tool, artificial bee colony (ABC) algorithm has attracted increasing attention in recent years. However, ABC still shows unsatisfactory performance in solving complex optimization problems. Although many ABC variants have been developed, the structural features of problems have been rarely considered, which have a significant effect on algorithm performance. Therefore, a novel ABC variant, called FLABC, is proposed by designing an online fitness landscape analysis technique. In this technique, the population is considered as a sample of the fitness landscape, and the idea of dispersion metric is used to identify the landscape features. According to the identified features, the solution search equations with distinct characteristics can be adaptively used, which helps adapt the search to the fitness landscape. In addition, FLABC has another two modifications, i.e., the dynamic multiple subpopulations and modified scout bee phase. In the experiments, FLABC is verified on two well-known test suites (CEC2013 and CEC2015) and two real-world optimization problems. Seven well-established ABC variants and five non-ABC variants are included in the performance comparison, and the results verify that FLABC has very competitive performance, especially on the functions with rugged fitness landscapes.