This research aims to improve the estimation of acoustic pressure in areas where direct sensor placement is not possible.
Optimized sensor weighting designed using evolutionary-based optimization algorithms.
Compared three algorithms: genetic algorithm (GA), bees algorithm (BA), and particle swarm optimization (PSO).
Conducted a numerical investigation to assess the performance of these algorithms.
The genetic algorithm showed the highest accuracy in pressure estimation compared to bees algorithm and particle swarm optimization.
Pressure estimation error was significantly reduced using optimized weights from the genetic algorithm, with a reported decrease in overall estimation error.
Particle swarm optimization provided moderate improvements over the baseline estimation method.