  The underwater radiated noise (URN) emanating from ships presents a
significant threat to marine mammals, given their heavy reliance on hearing for
essential life activities. The intensity of URN from ships is directly
correlated to the 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. The proposed framework addresses a fixed-path
voyage scheduling problem, incorporating two objective functions namely (i)
noise intensity levels and (ii) 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 using the genetic algorithm to
determine the Pareto solutions and subsequently the trade-off solution. The
effectiveness of the optimization framework is demonstrated via both simplified
tests and practical case studies involving a large container ship. A
comparative analysis illustrates the adaptability of the optimization framework
across different oceanic environments, affirming its potential as a robust tool
for reducing the URN from shipping.
