The 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.

Reinforcement Learning (RL) is an efficient learning method for solving problems that learning agents have no knowledge about the environment a priori. Ant Colony System (ACS) provides an indirect communication method among cooperating agents, which is an efficient method for solving combinatorial optimization problems. Based on the cooperating method of the indirect communication in ACS and the update policy of reinforcement values in RL, this paper proposes the Q-ACS multiagent cooperating learning method that can be applied to both Markov Decision Processes (MDPs) and combinatorial optimization problems. The advantage of the Q-ACS method is for the learning agents to share episodes beneficial to the exploitation of the accumulated knowledge and utilize the learned reinforcement values efficiently. Further, taking the visited times into account, this paper proposes the T-ACS multiagent learning method. The merit of the T-ACS method is that the learning agents share better policies beneficial to the exploration during agent's learning processes. Meanwhile, considering the Q-ACS and the T-ACS as homogeneous multiagent learning methods, in the light of indirect media communication among heterogeneous multiagent, this paper presents a heterogeneous multiagent RL method, the D-ACS that composites the learning policy of the Q-ACS and the T-ACS, and takes different updating policies of reinforcement values. The agents in our methods are given a simply cooperating way exchanging information in the form of reinforcement values updated in the common model of all agents. Owning the advantages of exploring the unknown environment actively and exploiting learned knowledge effectively, the proposed methods are able to solve both problems with MDPs and combinatorial optimization problems effectively. The results of experiments on hunter game and traveling salesman problem demonstrate that our methods perform competitively with representative methods on each domain respectively.