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Optimale Dämpferplatzierung in der mechanischen Fahrzeugaufhängung, entwickelt mit Bayesscher Optimierung und geschrieben in der Java-Programmiersprache (Optimal damper placement in automotive mechanical suspension implemented using Bayesian Optimization and written in Java)\n\nA multi-objective solution that utilizes Bayesian Optimization (BO) to optimize damper placement and parameters in an automotive suspension system. \n\nThe solution is to minimize three objectives—comfort (ISO 2631), vibration (frequency-weighted PSD), and handling (tire load variation)—across different road profiles (urban, highway, off-road) and damping profiles (linear, digressive, progressive).\n\n* Defines fixed vehicle parameters (e.g., sprung mass = 1500 kg, unsprung mass = 50 kg per wheel, tire stiffness = 200000 N/m).\n* Implements a Gaussian Process (GP) regression model \u0026 uses a Radial Basis Function (RBF) kernel\n* Supports incremental updates to the kernel matrix using the Sherman-Morrison formula for efficiency.\n* Provision for mean predictions and for uncertainty estimates, crucial for Bayesian optimization’s exploration-exploitation trade-off.\n* Uses three surrogate ensemble instances (one per objective: comfort, vibration, handling) to model the objective function.\n* Initializes with 30 random points, then iteratively selects new points using Simulated Annealing to maximize an active learning score combining Expected Hypervolume Improvement (EHVI), variance, and diversity.\n* Maintains Pareto front to store non-dominated solutions, with adaptive weights based on crowding entropy to balance objectives.\n* Uses EHVI to balance comfort, vibration, and handling, ensuring a diverse Pareto front.\n* The ensemble of GP, RF, and NN provides robust predictions and uncertainty estimates, critical for Bayesian optimization.\n* Incremental updates (e.g., Sherman-Morrison in GP) improve efficiency as new points are added.\n* Parallel evaluation of objectives reduces runtime for expensive simulations.\n* Simulated Annealing optimizes the acquisition function, balancing exploration and exploitation.\n* Handles invalid objectives (NaN, infinite, or excessively large) by retrying with perturbed parameters or assigning high penalties.\n\n---\n\nCheck [__*webpage*__](https://bo-optimal-damper-placement-2.netlify.app/) about the solution.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkmoraza%2Fbo-based-damper_placement_optimization--approach2","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkmoraza%2Fbo-based-damper_placement_optimization--approach2","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkmoraza%2Fbo-based-damper_placement_optimization--approach2/lists"}