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For the shortcoming of the basic ant colony algorithm which can only apply to discrete problems and by referring to artificial fish moving mechanism of fish swarm algorithm, a new ant colony algorithm for continuous problems was put forward. To reduce the time of process parameters optimization of sheet forming, the approximate model of RBF neural network was established between the process parameters and the forming target. Several segmental blank holder forces were set as the design variables in sheet forming, and thickening and thinning were regarded as forming target after sheet forming. Latin hypercube was used to sample, and the software of Dynaform was simulated to get the training samples. RBF neural network approximate mode between several segmental blank holder forces and forming quality was established based on the artificial fish swarm algorithm. The continuous ant colony algorithm was applied to optimize the model, and the optimal forces of several segmental blank holders were obtained. Taking oil pan for example, by contrast with the whole blank holder force, the method effectively improves the forming quality, and provides the basis for calculating the optimal blank holder force quickly.
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