Conventional ant colony optimization needs explicit evaluation function to boost searching process, so it cannot
effectively solve the systems whose optimization indices are unable or difficult to be quantified. Therefore, an interactive ant
system(IAS) that the objective function is determined by subjective human evaluation of the potential solutions in quantitative
way is proposed. According to the characteristics of human-computer interaction, the structure of IAS model, placement
method and update strategy of pheromone and user assessment approach are presented. Finally, IAS is tested in function
optimizations experiment that is designed as simulation environment for IAS and car styling draft design. The experimental
results show that the proposed method has significant operation efficiency and overcomes user fatigue as well.