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In this paper, an improvement for comprehensive learning particle swarm optimization (CLPSO) is presented, which is called adaptive comprehensive learning particle swarm optimization (A-CLPSO). Its ability to seek optimal point is verified by some kinds of test functions. Then, the A-CLPSO is used to guide antenna design and a new design model called semiautomatic design is introduced. This model contains two steps: rough design in which A-CLPSO is employed to obtain a digital configuration of an antenna, and precise design in which a continuous externality of the antenna is introduced according to the current distribution of the digital one and then its dimensions are optimized further by A-CLPSO. By the model, the designers' experience is no longer very important and the shape of the antenna is more reasonable than that obtained by traditional grid division. As an example, the design of a small multiband printed monopole antenna is carried out and the experiment results show the validity of the model.