Proposed by Prof. Dr. Derviş Karaboğa in 2005, inspired by the foraging behaviour of honey bee swarms.

Artificial Bee Colony Algorithm

A swarm intelligence optimization algorithm

The Artificial Bee Colony (ABC) algorithm simulates the task division and self-organization abilities of honey bees in their foraging activity. It was originally proposed by Prof. Dr. Derviş Karaboğa in 2005 for numerical optimization problems.

View courses How the algorithm works ↓

Employed beeImproves a known source in place
Onlooker beePicks richer sources with higher probability
Scout beeAbandons an exhausted source, searches a new region

Proposed by: Derviş Karaboğa
Affiliation: Erciyes Üniversitesi
Citations: 38,215
h-index: 57

01 — About

What is the ABC algorithm?

Each food source represents a candidate solution and its nectar amount the solution quality. The colony is split into employed, onlooker and scout bees.

How it works

ABC repeats three phases. Employed and onlooker bees exploit current solutions; scout bees provide exploration to escape local optima.

PHASE 1

Employed bees

Each employed bee is assigned to one food source (candidate solution). It produces a neighbouring candidate and replaces the current one if it is better (greedy selection).

PHASE 2

Onlooker bees

Onlookers choose sources based on the information shared by employed bees. Selection probability is proportional to source quality, so better sources are exploited more.

PHASE 3

Scout bees

A source that cannot be improved within limit trials is abandoned; its bee becomes a scout and generates a new random source. This is what lets the search escape local optima.

1: Generate and evaluate the initial food sources
2: repeat
3:     EMPLOYED BEE PHASE
4:         for each source produce a neighbour candidate, evaluate, select greedily
5:     ONLOOKER BEE PHASE
6:         choose a source with probability proportional to its fitness
7:         produce a neighbour candidate, evaluate, select greedily
8:     SCOUT BEE PHASE
9:         abandon a source not improved for 'limit' trials, produce a random new one
10:    memorize the best solution found so far
11: until (the maximum cycle number is reached)

Parameter	Description
SN	Number of food sources
limit	Trials before a source is abandoned
MCN	Maximum cycle number

Application areas

Numerical function optimizationThe field the algorithm was first introduced for (TR06, 2005).Neural network trainingTraining the weights of feed-forward networks with ABC.Clustering and data miningABC-based clustering and classification algorithms.Image processing and medical diagnosisPolyp detection, macular edema segmentation in OCT images.Digital filter designOptimization of IIR filter coefficients.Wireless sensor networksDeployment and routing problems.Robotic path planningRoute generation in obstacle-filled environments with ABC and ABCP.Combinatorial optimizationTravelling salesman and scheduling problems.Symbolic regressionModel discovery with Artificial Bee Colony Programming (ABCP).Protein foldingProtein structure prediction problems.

Selected publications

A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm
Derviş Karaboğa, Bahriye Basturk · Journal of Global Optimization · 2007 · DOI 10.1007/s10898-007-9149-x
7.614citations
AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION
Derviş Karaboğa · 2005
5.444citations
On the performance of artificial bee colony (ABC) algorithm
Derviş Karaboğa, B. Basturk · Applied Soft Computing · 2007 · DOI 10.1016/j.asoc.2007.05.007
3.679citations
A comparative study of Artificial Bee Colony algorithm
Derviş Karaboğa, Bahriye Akay · Applied Mathematics and Computation · 2009 · DOI 10.1016/j.amc.2009.03.090
3.209citations
A comprehensive survey: artificial bee colony (ABC) algorithm and applications
Derviş Karaboğa, Beyza Görkemli, Celal Öztürk, Nurhan Karaboğa · Artificial Intelligence Review · 2012 · DOI 10.1007/s10462-012-9328-0
1.975citations
Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems
Derviş Karaboğa, Bahriye Basturk · Lecture notes in computer science · 2007 · DOI 10.1007/978-3-540-72950-1_77
1.406citations
A modified Artificial Bee Colony algorithm for real-parameter optimization
Bahriye Akay, Derviş Karaboğa · Information Sciences · 2010 · DOI 10.1016/j.ins.2010.07.015
1.174citations
A novel clustering approach: Artificial Bee Colony (ABC) algorithm
Derviş Karaboğa, Celal Öztürk · Applied Soft Computing · 2009 · DOI 10.1016/j.asoc.2009.12.025
1.077citations

Software

Rust ABCRust Python ABCPython C# ABCPC#ABCoptimR Delphi ABCDelphi ABC DemoVisual demo Java ABCJava C ABCC MATLAB ABCMATLAB

06 — Monument

The Monument to the ABC of Artificial Intelligence

For the first time in Türkiye, a scientific discovery was turned into a monument on a university campus. It commemorates the Artificial Bee Colony algorithm, developed by Derviş Karaboğa in 2005.

Unveiled: 8 November 2024
Location: Erciyes University, Faculty of Engineering garden

The Monument to the ABC of Artificial Intelligence →

07 — Researchers

Researcher

Derviş Karaboğa

Prof. Dr. · Erciyes Üniversitesi