1. Overview
In this tutorial, we’ll learn about meta-heuristics and the artificial bee colony algorithm. However, before we get there, we’ll first take a quick look at some meta-heuristics concepts.
2. Meta-heuristics
Meta-heuristics, like heuristics, seek promising solutions to problems. A meta-heuristic algorithm, however, is generic and can deal with a variety of problems.
In the context of meta-heuristics, the characteristics of “non-measurable success” and “reasonable execution” remain the same as discussed for heuristics. However, meta-heuristics replace the problem-based design principle with the problem-independent design principle. Typically, meta-heuristic algorithms are designed for global optimization.
3. The Artificial Bee Colony Algorithm
This section is divided into three sub-sections. First general information regarding the artificial bee colony algorithm is given. Then, we present implementation steps and code examples for the artificial bee colony algorithm.
3.1. General Infomation
Dervis Karaboga proposed the artificial bee colony algorithm in 2005, inspired by the intelligent behavior of honey bees. It’s as simple as particle swarm optimization and differential evolution algorithms.
The artificial bee colony algorithm only uses standard control parameters like colony size and maximum cycle number. As an optimization tool, the artificial bee colony algorithm offers a population-based search procedure. In this procedure, individuals, called food positions, are modified over time by artificial bees.
So, the goal of the artificial bees is to find the ones with both the highest nectar amount and highest quality which refers to the best food source.
We can use the artificial bee colony algorithm to solve several optimization problems in various fields. The artificial bee colony algorithm’s operators, control parameters, and usage areas are given in the following figure:
3.2. Steps of the Artificial Bee Colony Algorithm
The artificial bee colony algorithm mimics the foraging behavior of natural honey bees. In the artificial bee colony algorithm, the bee colony family consists of three members. These’re employed bees, onlooker bees, and scout bees:
- Scout bees begin randomly searching for food sources. Once potential food sources are identified, scout bees become employed bees
- Employed bees exploit food sources while informing onlookers about the quality and quantity of food sources
- Onlooker bees rest at the hive and wait for information from employed bees
To share food information, employed bees perform a specific waggle dance, as shown in the following figure.
The steps of the artificial bee colony algorithm are summarized as follows:
- Produce initial food source
- Send employed bees to the food source
- Calculate probability values involved in the probabilistic selection
- Food source selection by onlookers based on the information provided by the employed bees
- Abandonment criteria: Limit and scout production
3.3. Coding for Artificial Bee Colony Algorithm
After understanding the concept and steps of the artificial bee colony algorithm, let’s look at the pseudocode of the artificial bee colony algorithm. The general pseudocode for the artificial bee colony algorithm is given as follows:
algorithm ArtificialBeeColonyAlgorithm():
// INPUT
// The formulation of the optimization problem to solve
// OUTPUT
// BestSolution = the best solution found by the algorithm
Initialize the set of possible solutions
while Termination requirements not met:
Select sites for local search
Recruit bees for the selected sites and to evaluate fitness
Select the bee with the best fitness as BestSolution
Assign the remaining bees to look randomly
Evaluate the fitness of remaining bees
Update probabilities
return BestSolution
We have multiple implementations of the artificial bee colony algorithm in different programming languages, such as Java, C, Python, and others.
4. Use Cases of the Artificial Bee Colony Algorithm
The artificial bee colony algorithm has several applications. For example, we can use this metaheuristic for solving capacitated vehicle routing problems.
Furthermore, the artificial bee colony algorithm works well for image segmentation. In this context, we can use this algorithm to compute pixel classification.
Another use case of the artificial bee colony algorithm is to solve hybrid flow shop scheduling problems.
Finally, according to industrial applications and literature, it’s well suited to assembly line balancing problems, redundancy allocation problems, training neural networks, and pattern classification.
5. Conclusion
In this tutorial, we learned about a meta-heuristic algorithm called the artificial bee colony algorithm. First and foremost, we refreshed our knowledge about meta-heuristics. Then, we looked into the implementation steps of the artificial bee colony algorithm. Finally, we discussed the applications and use cases of the artificial bee colony algorithm.