Acoustic pressure measurement in specific regions is essential for many engineering applications. However, due to factors such as inaccessibility, high temperature, or geometric constraints, sensors cannot always be placed directly near the target area. In such cases, measurements are taken from sensors located at accessible positions, and the acoustic pressure in the region of interest is subsequently estimated from the recorded data. Accurate reconstruction of this pressure field is therefore crucial. In this study, we address the estimation of acoustic pressure at arbitrary points within a rectangular enclosure by designing an optimized sensor weighting through evolutionary-based optimization algorithms. Three algorithms, namely genetic algorithm (GA), bees algorithm (BA), and particle swarm optimization (PSO), are compared in terms of their ability to determine the optimal sensor weights for accurate pressure estimation. A numerical investigation is presented to assess the overall performance of the three optimization algorithms.
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