What is ACO (Ant Colony Optimization) Algorithm

There are even increasing efforts in searching and developing algorithms that can find solutions to combinatorial optimization problems. In this way, the Ant Colony Optimization Meta-heuristic takes inspiration from biology and proposes different versions of still more efficient algorithms. Ant Colony Optimization (ACO): Overview Ant Colony Optimization (ACO) is a paradigm for designing metaheuristic algorithms for combinatorial optimization problems. The essential trait of ACO algorithms is the combination of a priori information about the structure of a promising solution with a posteriori information about the structure of previously obtained good solutions. ACO is a class of algorithms, whose first member, called Ant System, was initially proposed by Colorni, Dorigo and Maniezzo The main underlying idea, loosely inspired by the behavior of real ants, is that of a parallel search over several constructive computational threads based on local problem data and on a dynamic memory structure containing information on the quality of previously obtained result. The collective behavior emerging from the interaction of the different search threads has proved effective in solving combinatorial optimization (CO) problems. More specifically, we can say that “Ant Colony Optimization (ACO) is a population-based, general search technique for the solution of difficult combinatorial problems which is…

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