Constraint Consensus Based Artificial Bee Colony Algorithm for Constrained Optimization Problems

Table 2

Original ABC algorithm.

1	Initialize the food sources and evaluate the nectar amount (fitness) of food sources
	Send the employed bees to the current food source
	Iteration = 0
2	Do while (the termination conditions are not met)
	2.1  Employed Bees’ Phase/
	for (each employed bee)
	Find a new food source in its neighborhood following the Equation (6)
	Evaluate the fitness of the new food source, apply greedy selection
	end for
	2.2 Calculate the probability P for each food source according to the Equation (7)
	2.3 /Onlooker Bees’ Phase/
	for (each onlooker bee)
	Send onlooker bees to food sources depending on P
	Find a new food source in its neighborhood following the Equation (6)
	Evaluate the fitness of the new food source, apply greedy selection
	end for
	2.4 /Scout Bees’ Phase/
	if (any employed bee becomes scout bee)
	Send the scout bee to a randomly produced food source
	end if
	2.5 Memorize the best solution achieved so far
	Iteration = Iteration +1
	end while
3	Output the best solution achieved