An Immune-based Algorithm for Topology Optimization

Traditional shape optimization of engineering devices usually starts with an initial user-defined configuration of material. Optimization algorithms are then applied for optimizing objective functions of predefined parameters. While this approach can yield efficient results, it is essentially limited, since limitations in the initial design forbid the computational methods to explore different distributions of material as solutions for a given problem. In other words, the algorithms are not allowed to exhibit creativity in the design process. Topology optimization is a paradigm for optimization that allows such creativity to emerge. Instead of optimizing functions of user-defined parameters, this paradigm optimizes the material properties of each point of the design space, and its methods are theoretically able to describe all possible devices within a limited space. This work presents a new methodology for topology optimization, based on an evolutionary paradigm known as artificial immune systems. The proposed technique is capable of exploring the space locally as well as globally, efficiently searching for the optimal distribution of material. It also incorporates strategies for the evolution of smoother, more regular shapes, in order to generate physically feasible solutions for engineering problems.