Topological optimization of microstructure by means of bio-inspired methods

1. Abstract Optimization of structures in the macro scale is widely used nowadays. The goal of the paper is to apply optimization techniques to obtain better performance on the micro level. The presented approach opens new possibilities. The structures build with the use of materials with optimal microstructure can obtain the best performance. The microstructure can be optimized taking into account loads of the macro structure. Optimization of the microstructure is not easy currently, but in the future, the presented approach may be used with success when performance of the structure is very important. A bio-inspired method based on the artificial immune system (AIS) is used to solve the optimization problem. Immune computing provides a great probability of finding the global optimum. It is developed on the basis of a mechanism discovered in biological immune systems. An immune system is a complex system which contains distributed groups of specialized cells and organs. The main purpose of the immune system is to recognize and destroy pathogens funguses, viruses, bacteria and improper functioning cells. The artificial immune system takes only a few elements from the biological immune systems. Mutation of the B cells, proliferation, memory cells, and recognition by using the B and T cells are used the most frequently. The unknown global optimum is represented by the searched pathogen. The memory cells contain design variables and proliferate during the optimization process. The B cells created from memory cells undergo the mutation. The B cells evaluate and better ones exchange memory cells. The optimal topology is generated by the level set approach. The crowding mechanism is used the diverse between memory cells is forced. A new memory cell is randomly created and substitutes the old one, if two memory cells have similar design variables. The crowding mechanism allows finding not only the global optimum but also other local ones. Additional the Gaussian mutation is used in this approach. The paper presents methodology, algorithm of optimization and numerical examples. 2.