Improving the diversity of topology-optimized designs by swarm intelligence

Although additive manufacturing can produce nearly any geometry, users have limited choices in the designs. Topology optimization can create complex shapes, but it provides only one solution for one problem, and existing design exploration methods are ineffective when the design space is huge and high-dimensional. Therefore, this paper develops a new generative design method to improve the diversity of topology-optimized designs. Based on the observation that topology optimization places materials along the principal directions to maximize stiffness, this paper creates a rule of principal direction and applies it to swarm intelligence for form-finding. The shapes got by the swarming process possess both randomness and optimality. After they are further optimized, the final designs have high diversity. This is the first time integrating structural stiffness as a swarm principle to influence the collective behavior of decentralized, self-organized systems. The experimental results show that this method can generate interesting designs that have not been seen in the literature. Some results are even better than those got by the original topology optimization method, especially when the problem is more complex. This work not only allows users to choose unique designs according to their preference, but also helps users find better designs for their application.