Sensor Placement Optimization Based on an Improved Inertia and Adaptive Particle Swarm Algorithm During an Explosion

An explosion field is random and nonuniform, and test sensors can be damaged by fragments during blast wave monitoring. When accurately and comprehensively obtaining the experimental data and the dynamic distributions from an explosion field, the quantity and positioning of sensors during blast wave monitoring are important parameters. In this paper, an optimization method of sensor placement based on an improved inertia and adaptive particle swarm optimization (IIAPSO) algorithm is proposed to solve this problem. This work considers two new aspects: 1) the adaptive mutation mechanism and the inertia weight into classic particle swarm optimization (PSO) and 2) the propagation law of blast waves, the data errors and the probability of sensor damage during IIAPSO. These mechanisms are employed to enhance the global search ability and to increase data accuracy. First, the 12 benchmark functions are utilized to test the performance of the IIAPSO. The performance of the IIAPSO is compared with PSO, linear decreasing weighted particle swarm optimization (LDW-PSO) and adaptive particle swarm optimization (APSO) in terms of parameter accuracy and convergence speed. The results confirm that the proposed IIAPSO is more successful than PSO, LDW-PSO and APSO algorithms. Finally, the IIAPSO is used to optimize the sensor placement in an explosion field. The simulation and experimental results show that the feasibility of this algorithm is demonstrated.