Accelerating the ANT Colony Optimization By Smart ANTs, Using Genetic Operator
This is an incremental improvement for optimization problems, potentially benefiting researchers and practitioners in fields like scheduling or routing.
The paper tackled the problem of slow convergence and inefficiency in Ant Colony Optimization (ACO) by integrating a genetic operator to accelerate ant decision-making, resulting in a hybrid algorithm that outperforms other versions in both speed and accuracy.
This paper research review Ant colony optimization (ACO) and Genetic Algorithm (GA), both are two powerful meta-heuristics. This paper explains some major defects of these two algorithm at first then proposes a new model for ACO in which, artificial ants use a quick genetic operator and accelerate their actions in selecting next state. Experimental results show that proposed hybrid algorithm is effective and its performance including speed and accuracy beats other version.