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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 ...
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.
The humanoid ant algorithm (HUMANT) [1] is an ant colony optimization algorithm. The algorithm is based on a priori approach to multi-objective optimization (MOO), which means that it integrates decision-makers preferences into optimization process. [2] Using decision-makers preferences, it actually turns multi-objective problem into single ...
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.
Graduated optimization digressively "smooths" the target function while optimizing. Ant colony optimization (ACO) uses many ants (or agents) to traverse the solution space and find locally productive areas. The cross-entropy method (CE) generates candidate solutions via a parameterized probability distribution. The parameters are updated via ...
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]
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.
Cuckoo Optimization Algorithm COA Nature-inspired Bio-inspired 2011 [32] Stochastic Diffusion Search SDS 2011 Teaching-Learning-Based Optimization TLBO Nature-inspired Human-based 2011 [33] Bacterial Colony Optimization BCO 2012 [34] Fruit Fly Optimization FFO 2012 Krill Herd Algorithm KHA Nature-inspired Bio-inspired 2012 [35]