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There exist numerous image retrieval systems perform a fast similarity search in the image databases, but the quality of the outcomes provided by color histogram-based image search is usually rather limited. In this paper, an innovative approach based on blocking wavelet-histogram image similarity retrieval method and particle swam optimization (PSO), which is proposed as a solution to the problem of intelligent retrieval of images in large image databases. The problem is recast to a discrete optimization one, where a suitable speed and position of particle is defined through a customized PSO. Farther on, in virtue of the new computation model, a fitness function which combines blocking wavelet transformation information and the Euclidean distance of color histogram is constructed. The experimental results show that the proposed algorithm is feasible and effective to the similarity search in images database.