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An artificial beehive algorithm for continuous optimization
Author(s) -
Muñoz Mario A.,
López Jesús A.,
Caicedo Eduardo
Publication year - 2009
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20376
Subject(s) - beehive , benchmark (surveying) , computer science , set (abstract data type) , algorithm , swarm intelligence , artificial bee colony algorithm , swarm behaviour , artificial intelligence , bees algorithm , mathematical optimization , metaheuristic , machine learning , mathematics , particle swarm optimization , biology , botany , geodesy , programming language , geography
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 swarm‐based 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.