ACO Algorithm with Additional Reinforcement

The aim of the paper is to develop the functionality of the ant colony optimization (ACO) algorithms by adding some diversification such as additional reinforcement of the pheromone. This diversification guides the search to areas in the search space which have not been yet explored and forces the ants to search for better solutions. In the ACO algorithms [1],[2] after the initialization, a main loop is repeated until a termination condition is met. In the beginning ants construct feasible solutions, then the pheromone trails are updated. Partial solutions are seen as states: each ant moves from a state i to another state j corresponding to a more complete partial solution.