Microphone Array Optimization With Multistrategy Sparrow Search Algorithm

Acoustic cameras are widely used in noise localization, but existing techniques face challenges such as excessive sensor requirements and insufficient localization accuracy. This study builds upon the Sparrow Search Algorithm (SSA), renowned for its adaptability and excellence in multi-parameter structural optimization, to enhance localization accuracy while reducing sensor usage. However, when addressing planar matrix topology problems, SSA exhibits two major shortcomings: (i) slow convergence speed; (ii) susceptibility to local optima. This study resolves these issues through two key improvements: (i) introducing a novel propagation mechanism to enhance global optimization performance; (ii) employing a fast iterative scaling threshold algorithm (FISTA) based on population dynamics to accelerate convergence. Qualitative comparisons with SSA demonstrate that the proposed algorithm achieves 68% faster convergence and a 10.47% higher probability of obtaining optimal solutions. Both simulations and experiments validate the effectiveness of this approach.