Optimal placement of sensors for structural health monitoring using improved genetic algorithms

The global optimization of sensor locations for structural health monitoring systems is studied in this paper. First, the performance function based on damage detection is presented. Then, genetic algorithms (GAs) are adopted to search for the optimal locations of sensors. However, the simple GAs can result in infeasible solutions to the problem. Some improved strategies are presented in this paper, such as crossover based on identification code, mutation based on two gene bits, and improved convergence. The analytical results from the improved genetic algorithm are compared with the penalty function method and the forced mutation method. It is concluded that the convergence speed with the proposed improved genetic algorithm is faster than that with the penalty function method and the forced mutation method, and the result of placement optimization is better.