The wireless Sensor network has recently become an intensive research focus due to its potential applications many years. Sensor Placement is one of the most important issues in wireless Sensor networks. An efficient Placement scheme can enhance the quality of monitoring in wireless Sensor networks by increasing the coverage rate of interested area. This paper presents an efficient method based on parallel genetic algorithms to solve a Sensor Placement optimization problem. We modify the general master-slave parallel genetic algorithm to improve the convergence rate of this optimization method. The results indicate the effectiveness of the proposed method in comparison with genetic algorithm, general parallel genetic algorithm, and some well-known evolutionary algorithms.

In this study, optimal Sensor Placement (OSP) which plays a key role in the health monitoring of large-scale structures, is investigated using the genetic algorithm (GA). The OSP is among permutation problems that it's challenging to define the crossover operator in this kind of problem. In this study, a new hybrid crossover operator is proposed to find the optimal location for Sensors and two different strategies are investigated for selecting members to form the next generation population. Also, the two-structure coding method has been used instead of the typical binary coding method to create the chromosomes of the population members. The objective function and fitness is defined based on the modal assurance criterion (MAC) matrix that is calculated with identified mode shapes and analytical mode shapes. The efficiency of the proposed method was investigated on a high-rise structure. The results show that the mode shapes identified by the optimal Placement obtained from the proposed method are identical to the analytical mode shapes of the finite element model. Also, the comparison between the Sensor locations obtained by conventional operators and the proposed operator shows that the proposed hybrid crossover operator outperforms other operators in terms of accuracy and convergence speed.