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This paper proposes fuzzy systems design by clustering-aided ant colony optimization (ACO) algorithm (CACO). The objective of CACO is to improve both the design efficiency of fuzzy systems and its performance. In CACO, structure of a fuzzy system, including the number of rules and fuzzy sets in each input variable, is created on-line by a newly proposed fuzzy clustering. In contrast to conventional grid-type partition, the antecedent part of a fuzzy system is flexibly partitioned, and the phenomenon of highly overlapped fuzzy sets is avoided. Once a new rule is generated, the consequence is selected from a list of candidate control actions by ACO. In ACO, the route of an ant is regarded as a combination of consequent actions selected from every rule. A pheromone matrix among all candidate actions is constructed and an on-line learning algorithm for heuristic value update is proposed. Searching for the best one among all consequence combinations involves using the pheromone matrix and heuristic values. To verify the performance of CACO on fuzzy systems design, simulations on nonlinear system control, water bath temperature control and chaotic system control are performed. Simulations on these problems and comparisons with other algorithms have demonstrated the performance of CACO

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