Knowledge acquisition is a long-standing problem in fuzzy-rule-based systems. In spite of the existence of several approaches, much effort is still required to increase the efficiency of the learning process. This study introduces a new method for the fuzzy-rule evolution that forms an expert system knowledge: the knowledge acquisition with a swarm-intelligence approach (KASIA). Specifically, this strategy is based on the use of particle-swarm optimization (PSO) to obtain the antecedents, consequences, and connectives of the rules. To test the feasibility of the suggested method, the inverted-pendulum problem is studied, and results are compared for two of the most extensively used methodologies in machine learning: the genetic-based Pittsburgh approach and the Q-learning-based strategy, i.e., state-action-reward-state-action (SARSA). Moreover, KASIA is analyzed as a learning strategy in fuzzy-rule-based metascheduler design for grid computing, and performance is compared with other scheduling strategies based on genetic learning and existing scheduling approaches, i.e., EASY-backfilling and ESG+local periodical search. To be more precise, simulation results prove the fact that the proposed strategy outperforms classical learning approaches in terms of final results and computational effort. Furthermore, the main advantage is the capability to control convergence and its simplicity.

This approach presents a fully distributed clustering solution for wireless sensor networks. It relies on the results of an interval type-2 fuzzy logic system that gives each node the chance to be a cluster head. Taking into account the limited computational resources of the sensors, this inference system has been carefully adapted to be run in each node through a sampling process of the entire solution space of the fuzzy system. The input variables of the system are obtained from the information that each node derives from its performance metrics and those related to its neighbors. The acquisition of these last data does not incur in any additional control packets. The results obtained show a significant improvement in the network lifetime when compared with other recent approaches. This improvement takes place even when contrasting with centralized methods.