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In the ant colony optimization algorithms, an artificial ant is a simple computational agent that searches for good solutions to a given optimization problem. To apply an ant colony algorithm, the optimization problem needs to be converted into the problem of finding the shortest path on a weighted graph. In the first step of each iteration ...
The first multi-objective ant colony optimization (MOACO) algorithm was published in 2001, [4] but it was based on a posteriori approach to MOO. The idea of using the preference ranking organization method for enrichment evaluation to integrate decision-makers preferences into MOACO algorithm was born in 2009. [5]
Ant colony optimization (ACO), introduced by Dorigo in his doctoral dissertation, is a class of optimization algorithms modeled on the actions of an ant colony. ACO is a probabilistic technique useful in problems that deal with finding better paths through graphs.
Artificial Bee Colony ABC Nature-inspired Bio-inspired 2005 [12] Ant Colony Optimization: ACO Nature-inspired Bio-inspired 2006 [13] Glowworm Swarm Optimization: GSO Nature-inspired Swarm-based 2006 [14] Shuffled Frog Leaping Algorithm SFLA Nature-inspired Bio-inspired 2006 [15] Invasive Weed Optimization IWO Nature-inspired Plant-based 2006 [16]
The ant colony optimization algorithm is a probabilistic technique for solving computational problems that can be reduced to finding good paths through graphs.Initially proposed by Marco Dorigo in 1992 in his PhD thesis, [1] [2] the first algorithm aimed to search for an optimal path in a graph based on the behavior of ants seeking a path between their colony and a source of food.
MIDACO (Mixed Integer Distributed Ant Colony Optimization) is a software package for numerical optimization based on evolutionary computing.MIDACO was created in collaboration of European Space Agency and EADS Astrium to solve constrained mixed-integer non-linear (MINLP) space applications.
Bacterial colony optimization; Barzilai-Borwein method; Basin-hopping; Benson's algorithm; Berndt–Hall–Hall–Hausman algorithm; Bin covering problem; Bin packing problem; Bland's rule; Branch and bound; Branch and cut; Branch and price; Bregman Lagrangian; Bregman method; Broyden–Fletcher–Goldfarb–Shanno algorithm
The network of trails functions as a shared external memory for the ant colony. [8] In computer science, this general method has been applied in a variety of techniques called ant colony optimization, which search for solutions to complex problems by depositing "virtual pheromones" along paths that appear promising. [9]