continuous ACO. In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. Initially proposed by Marco Dorigo in 1992 in his PhD thesis,[1][2] the first algorithm was aiming 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. The original idea has since diversified to solve a wider class of numerical problems, and as a result, several problems have emerged, drawing on various aspects of the behavior of ants. From a broader perspective, ACO performs a model-based search [3] and share some similarities with Estimation of Distribution

YPEA104 ACOR
............\ACOR
............\....\acor.m
............\....\license.txt
............\....\main.m
............\....\RouletteWheelSelection.m
............\....\Sphere.m
............\license.txt
............\ACOR\acor.asv

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