SUMMARYThe paper introduces a novel pheromone update strategy to improve the functionality of ant colony optimization algorithms. This modification tries to extend the search area by an optimistic reinforcement strategy in which not only the most desirable sub-solution is reinforced in each step, but some of the other partial solutions with acceptable levels of optimality are also favored. therefore, it improves the desire for the other potential solutions to be selected by the following artificial ants towards a more exhaustive algorithm by increasing the overall exploration. The modifications can be adopted in all ant-based optimization algorithms; however, this paper focuses on two static problems of travelling salesman problem and classification rule mining. To work on these challenging problems we considered two ACO algorithms of ACS (Ant Colony System) and AntMiner 3.0 and modified their pheromone update strategy. As shown by simulation experiments, the novel pheromone update method can improve the behavior of both algorithms regarding almost all the performance evaluation metrics.

The paper deals with a modification in the learning phase of AntNet routing algorithm, which improves the system adaptability in the presence of undesirable events. Unlike most of the ACO algorithms which consider reward-inaction reinforcement learning, the proposed strategy considers both reward and penalty onto the action probabilities. As simulation results show, considering penalty in AntNet routing algorithm increases the exploration towards other possible and sometimes much optimal selections, which leads to a more adaptive strategy. The proposed algorithm also uses a selfmonitoring solution called Occurrence-Detection, to sense traffic fluctuations and make decision about the level of undesirability of the current status. The proposed algorithm makes use of the two mentioned strategies to prepare a selfhealing version of AntNet routing algorithm to face undesirable and unpredictable traffic conditions.

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