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Employed bee

As explained in Optimization Algorithms: AI techniques for design, planning, and control problems

An employed bee is an artificial-bee-colony agent that searches the neighborhood of an associated food source for improved solutions.

An employed bee is an artificial-bee-colony agent assigned to a food source, or candidate solution. It searches that solution’s neighborhood for an improvement, providing local exploitation while the colony explores the wider search space. section 10.5

Within artificial bee colony optimization, employed bees work alongside two complementary roles:

  • Employed bees refine their associated food sources through local search. section 10.5
  • Onlooker bees select sources probabilistically according to the fitness information shared by employed bees. section 10.5
  • When a source cannot improve after a specified number of trials, its employed bee becomes a scout and searches for a new source. section 10.5

Neighborhood improvement on Rosenbrock optimization

A candidate position is modified in one randomly selected dimension using a partner bee and a random factor. The new position is clipped to the allowed bounds, evaluated, and retained only when its fitness improves; otherwise, the bee’s trial counter increases. A six-source example shows a candidate improving from objective value 438.49 to 15.15, making it more likely to receive onlooker attention. section 10.5

Algorithm 10.1 Artificial bee colony algorithm
Initialization Phase: population of candidate solutions (food sources) are initialized 
REPEAT
   Forager Bee Phase: Each forager bee goes to a food source in her memory and determines a closest source, then evaluates its nectar amount and dances in the hive
   Onlooker Bee Phase: Each onlooker bee watches the dance of forager bees and chooses one of their sources depending on the dances, and then goes to that source. After choosing a neighbor around that, she evaluates its nectar amount.
   Scout Bee Phase: Abandoned food sources are determined and are replaced with the new food sources discovered by scout bees.
   Memorize the best food source (solution) achieved so far.
UNTIL (termination criteria are met)

Follow the artificial bee colony cycle from initialized food sources through the forager, onlooker, and scout phases, including best-solution retention. section 10.5

How Optimization Algorithms: AI techniques for design, planning, and control problems covers it

How deep it goes
Explains roles, neighbor updates, greedy acceptance, trial counting, and scout conversion, but does not derive convergence guarantees.
Examples
Demonstrates employed-bee updates with the Rosenbrock function, candidate food-source tables, selection probabilities, and Python implementations.
What it assumes
Assumes basic optimization vocabulary while introducing ABC’s exploration–exploitation balance through bee roles and food-source metaphors.
What you can do after
Build it from scratch: implement neighborhood updates, bounds, greedy replacement, and trial tracking for ABC agents.
How it connects
Connects employed bees to artificial bee colony optimization, onlooker bees, scout bees, fitness, exploration, exploitation, and local optima.
Cover of Optimization Algorithms: AI techniques for design, planning, and control problems
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