Abstract: Ant Colony Optimization (ACO) is a paradigm for designing met heuristic 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. Heuristic algorithms are algorithms which, in order to escape from local optima, drive some basic heuristic: either a constructive heuristic starting from a null solution and adding elements to build a good complete one, or a local search heuristic starting from a complete solution and iteratively modifying some of its elements in order to achieve a better one. The met heuristic part permits the low-level heuristic to obtain solutions better than those it could have achieved alone even if iterated. Usually, the controlling mechanism is achieved either by constraining. The particular way of defining components and associated probabilities is problem-specific, and can be designed in Different ways, facing a trade-off between the specificity of the information used for the conditioning and the number of solutions which need to be constructed before effectively biasing the probability distribution to favor the emergence of good solutions. Different applications have favored either the use of conditioning at the level of decision variables, thus requiring a huge number of iterations before getting a precise distribution, or the computational efficiency, thus using very coarse conditioning information.
Keywords: ACO, Swarm, Neural Networks, Fuzzy System, Swarm Intelligence, IEEE
| DOI: 10.17148/IARJSET.2020.7101