Vibration issues in mechanical structures are widespread across mechanical equipment, posing significant challenges to the stability and safety of various mechanical systems. Currently, acoustic metamaterials have been extensively applied to vibration control in common mechanical structures. However, multi-modal adaptive regulation of structures in complex and variable environments remains a challenge. This paper proposes an Adaptive Nonlinear Particle Swarm Optimization (ANL-PSO) vibration control algorithm based on the local resonance bandgap mechanism. By introducing a nonlinear inertia weight derived from population fitness, the algorithm balances global exploration and local exploitation capabilities, thereby enhancing vibration control performance in complex real-world environments. Additionally, a multi-modal zoning control strategy is designed based on the optimization of piezoelectric patch layouts. Experimental results indicate that the proposed ANL-PSO algorithm demonstrates enhanced local optima avoidance capabilities while exhibiting higher convergence speed and optimization precision than conventional PSO. Specifically, frequency-band vibration suppression performance shows a minimum 12.48% improvement over PSO. Through optimized multimodal vibration control, the algorithm achieves over 18 dB reduction in resonance bandgap amplitudes for two plate structure modes, verifying its effectiveness in coordinated structural dynamics optimization.