Distant Relative Genetic Algorithm-Based Structural Reliability Optimization

In this study, safety margin explicit equation has been established using random variables (i.e., the engineering conditions, structure parameters, structural strength, and external load), and the genetic algorithm (GA)-based structural reliability optimization design has been addressed subsequently. Though the conventional adaptive GA can change automatically with fitness, it is still not unsatisfactory in sufficiently improving the algorithm convergence speed, especially for complex structures. This article presents an improved adaptive technology termed as the distant relative genetic algorithm (DRGA), in which the distant relative pointer and immunity operators can effectively improve the search performance of the GA. In early evolution, by means of cross controlling and avoiding pairing between individuals with the same genes, the methodology prevents the isogenic individuals expanding locally. Besides, the revised algorithm is able to jump out of the local optimal solution, thus ensuring the realization of a fast global convergence. An example based on wing box structure optimization has been demonstrated using the improved method, and the calculation results show that this strategy makes the GA more effective in dealing with the constraint optimization issues.