Hybridization of genetic algorithm with immune system for optimization problems in structural engineering

Optimization is the task of getting the best solution among the feasible solutions. There are many methods available to obtain an optimized solution. Genetic algorithm (GA), which is a heuristic type of optimization method, is discussed in this paper. The focus of the paper is the use of GA for large dimensionality design problems, where computational efficiency is a major concern. The motivation of this paper is to hybridize GA with an immune system mechanism by avoiding the implementation of penalty constants, which are highly sensitive to the choice of algorithm parameters. The principal advantage of the immune system is in its seamless integration with GA-based search for optimal design. It is being hybridized with the immune system mechanism. The hybrid GA and immune system is applied for the design of the optimal mix of high-performance concrete (HPC), which is still based on trial mix and for which no rigorous mathematical approach is available. As such, to infer the values of strength and slump, a wavelet back propagation neural network or wavelet neural network is used for any HPC mix. It is necessary to minimize the cost of HPC/unit weight of HPC subjected to strength and slump constraints. The interwoven algorithm is also applied to obtain optimal sectional areas for minimum weight of space trusses subjected to static loading. Formian programming language is used for the generation of the space trusses, and Feast package is used for the static analysis of the trusses. In addition to the induction of immune system in the GA for constraint handling, it is being applied in this particular application for improving the search of GA in obtaining the best optimal solution. For obtaining the optimal sections of space trusses subjected to earthquake loading, SAP 90 package is used, and reliable results are obtained.