
Artificial Bee Colony (ABC) algorithm is a powerful optimization algorithm that simulates the task division and self-organization abilities of honey bees in their foraging activity. ABC algorithm has attracted the attention of researchers and has been applied to many problems in various fields successfully. ABC algorithm was originally proposed to solve numeric optimization problems and researchers have made some arrangements in the algorithm to solve combinatorial, constrained, multi-objective problems, etc. In this chapter, a tutorial on the basic concepts of the ABC algorithm is given including the explanations of each phase and its control parameters. Source-codes of the ABC algorithm in Matlab and C++ programming languages are provided for the readers who want to apply the algorithm to solve the problems in their research fields. Search behaviour of the ABC algorithm balancing the exploration and exploitation is analysed by showing step-by-step problem solving procedure on a numeric test problem.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 589 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 0.1% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 0.1% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
