Ant Algorithms: Theory and Applications
Programming and Computing Software
International Journal of Approximate Reasoning
Zero-order TSK-type fuzzy system learning using a two-phase swarm intelligence algorithm
Fuzzy Sets and Systems
Generating Routes with Bio-inspired Algorithms under Uncertainty
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Counter-ant algorithm for evolving multirobot collaboration
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Self-Organizing Multirobot Exploration through Counter-Ant Algorithm
IWSOS '08 Proceedings of the 3rd International Workshop on Self-Organizing Systems
International Journal of Approximate Reasoning
A case study for learning behaviors in mobile robotics by evolutionary fuzzy systems
Expert Systems with Applications: An International Journal
Ant colony optimization incorporated with fuzzy Q-learning for reinforcement fuzzy control
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Fuzzy Sets and Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Designing fuzzy-rule-based systems using continuous ant-colony optimization
IEEE Transactions on Fuzzy Systems
Information Sciences: an International Journal
Recurrent fuzzy system design using elite-guided continuous ant colony optimization
Applied Soft Computing
Learning cooperative TSK-0 fuzzy rules using fast local search algorithms
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
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Within the field of linguistic fuzzy modeling with fuzzy rule-based systems, the automatic derivation of the linguistic fuzzy rules from numerical data is an important task. In the last few years, a large number of contributions based on techniques such as neural networks and genetic algorithms have been proposed to face this problem. In this article, we introduce a novel approach to the fuzzy rule learning problem with ant colony optimization (ACO) algorithms. To do so, this learning task is formulated as a combinatorial optimization problem. Our learning process is based on the COR methodology proposed in previous works, which provides a search space that allows us to obtain fuzzy models with a good interpretability–accuracy trade-off. A specific ACO-based algorithm, the Best–Worst Ant System, is used for this purpose due to the good performance shown when solving other optimization problems. We analyze the behavior of the proposed method and compare it to other learning methods and search techniques when solving two real-world applications. The obtained results lead us to remark the good performance of our proposal in terms of interpretability, accuracy, and efficiency. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 433–452, 2005.