An artificial beehive algorithm for continuous optimization

Abstract

This paper presents an artificial beehive algorithm for optimization in continuous search spaces based on a model aimed at individual bee behavior. The algorithm defines a set of behavioral rules for each agent to determine what kind of actions must be carried out. Also, the algorithm proposed includes some adaptations not considered in the biological model to increase the performance in the search for better solutions. To compare the performance of the algorithm with other swarmbased Techniques, we conducted statistical analyses by using the so-called t test. This comparison is done with several common benchmark functions. © 2009 Wiley Periodicals, Inc.