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We present an adaptive approach for improving the performance in randomly distributed Wireless Sensor Networks (WSNs). The goal is to find the optimal routing not only to maximize the lifetime of the network but also to provide real-time data transmission services. Considering a wireless sensor network where the nodes have limited energy, we propose a novel model Energy & Delay based on Ant Colony Optimization (ACO) algorithm (E&D ANTS) to minimize the time delay in transferring a fixed number of data from the source nodes to the destination nodes in an energy-constrained manner. In the algorithm, an amount of artificial ants randomly explored the network and exchanged collected network information to periodically update ant routing-tables which were obtained by having integrated partial pheromones and heuristic values. Our study is focused on influence functions of pheromones. Because of the tradeoff of energy and delay in wireless network systems, we propose the Reinforcement Learning (RL) algorithm to train our model. The simulation results show that our method boasts undoubtedly a number of attractive features, including adaptation, robustness and stability. [ABSTRACT FROM AUTHOR]