Research Article
Constraint Consensus Based Artificial Bee Colony Algorithm for Constrained Optimization Problems
| 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 |
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