A multi-objective optimization framework for reducing the impact of ship noise on marine mammals

The underwater radiated noise (URN) emanating from ships presents a significant threat to marine mammals, given their heavy reliance on hearing. The intensity of URN from ships is correlated to their speed, making speed reduction a crucial operational mitigation strategy. This paper presents a new multi-objective optimization framework to optimize the ship speed for effective URN mitigation without compromising fuel consumption. This framework addresses a fixed-path voyage scheduling problem, incorporating two objective functions namely, noise intensity levels and fuel consumption. The optimization is performed using the state-of-the-art non-dominated sorting genetic algorithm under voyage constraints. A 2D ocean acoustic environment, comprising randomly scattered marine mammals of diverse audiogram groups and realistic conditions, including sound speed profiles and bathymetry, is simulated. To estimate the objective functions, we consider empirical relations for fuel consumption and near-field noise modeling together with a ray-tracing approach for far-field noise propagation. The optimization problem is solved to determine the Pareto solutions and the trade-off solution. The effectiveness of the framework is demonstrated via practical case studies involving a large container ship. A comparative analysis illustrates the adaptability of the framework across different oceanic environments, affirming its potential as a robust tool for reducing the URN from shipping.