The increasing adoption of computational design methods in performance-based architectural design has expanded their application across multiple domains, including structural engineering, building physics, and acoustic optimization. Resource constraints and the pursuit of reduced material and energy consumption have accelerated the use of optimization strategies in architecture. The principal novelty of this study is the introduction of a multi-objective computational framework that integrates Performance-Based Design (PBD), parametric modeling, and genetic algorithm optimization to enhance the acoustic performance of opera houses. Unlike conventional approaches that apply acoustic analysis only in final design stages or optimize a single acoustic criterion, this framework simultaneously optimizes competing objectives—reverberation time (RT), singer sound pressure level (SPL), and orchestra SPL—within a unified parametric workflow.

Architectural and acoustical standards were first identified and parametrically modeled to define the initial hall volume, incorporating variables directly influencing acoustic quality. Digital design tools such as Rhinoceros, Grasshopper, and Pachyderm Acoustic enabled the simulation and evaluation of multiple design alternatives before implementation. Key acoustic criteria included maintaining RT within 1.3–1.8 seconds, maximizing the singer's SPL, controlling orchestra SPL above 20 dB, and preventing excessive low-frequency RT.

Compared with conventional trial-and-error approaches, this integrated methodology reduced design time and costs while delivering data-driven, performance-oriented solutions. The study demonstrates the potential of parametric adaptability and multi-objective optimization to achieve acoustically optimized and sustainable performance halls. Future research should expand the framework to include additional acoustic parameters and integrate real-time adaptive systems for multipurpose venues.