Abstract:
In computing and operations research, the ant colony optimization algorithm (ACO) is a
probabilistic approach to addressing problems that can often be reduced to locating optimal paths
via graphs. In this metaphor, robotic ants stand in for the multi-agent techniques that were
modelled after the actions of real ants. In the world of biological ants, pheromone-based
communication is often the norm. Combinations of artificial ants and local search algorithms
have become the de facto standard method for many optimization tasks requiring graphs, such as
vehicle routing and internet routing. In order to approximate solutions to hard optimization
problems, a population-based metaheuristic known as ant colony optimization (ACO) can be
applied. In ACO, an optimization issue is posed, and a colony of virtual "ants" searches for a
solution. In this paper, we apply the ACO algorithm to the puzzle-solving method commonly
known as sudoku. This strategy eliminates the need for any additional algorithms beyond the one
used to solve the original problem. This one will help us save time and find answers to difficult
problems with just a single mouse click.. .
