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Based on the properties of the cloud model on the process of transforming a qualitative concept to a set of quantitative numerical values, an adaptive computational intelligence optimization algorithm is proposed by analyzing the correspondence between search characteristics and cloud models. In the proposed algorithm, the feature parameters of solution sets are created by a multidimensional backward cloud generator, and then adaptively adjusted based on the change of the elite solution candidates. The result is then used by a forward cloud generator to produce the solution set of next generation. No any search parameters are predefined, and, no matter what the initial solution set is, the whole system can adaptively search for solutions to various complicated optimization problems. Two illustrative examples of parameter identification for Lorenz and Chen chaotic systems are given with the aid of an appropriate evaluation function. Numerical simulation and comparisons with the other two existing algorithms demonstrate the effectiveness and feasibility of the proposed algorithm.