STM Article Repository

This paper proposes a Hybridized Ant Colony Optimization (HACO) algorithm whose main focus is based on harnessing the strengths of the AS, ACS, and MMAS previously proposed by various researchers at one time or another. The HACO algorithm for solving optimization problems in this document employs new Transition Probability relations with a Jump transition probability relation, that also indicates the point or path where the desired optimum value has been met. Also it presents a new pheromone updating rule and the pheromone evaporation residue, which calculates the amount of pheromone left after updating. This acts as a guide for the next ant traversing the path and various local search approaches. We notice that the HACO algorithm's computational efficiency finds very workable answers in a short time, as the algorithm has been evaluated on a number of combinatorial optimization problems, and outcomes have been shown to compare favourably with analytical results.