Multiobjective optimization of very thin noise barriers

Abstract

Using multi-objective (MO) optimization gives rise to a more comprehensive design scenario by providing a set of trade-off optimal solutions, popularly known as Pareto-optimal solutions. Due to the multiplicity in solutions, these problems are proposed to be solved suitably using evolutionary algorithms (EA’s), which are relatively new but very powerful techniques used to find solutions to many real- world search and optimization problems [1-9].

The approach of this work is based on the evolutionary multi-objective optimization (EMO) of very thin noise barrier models with improved performance, whose designs were previously studied in [10] by means of single- objective optimization framework for idealized single-wire barrier models. One of the criterions involves maximization of the noise attenuation efficiency, while the remaining one deals with the minimization of the amount of material used in manufacturing the barrier as representative of the its total length. This work deals with optimization problems performed by the combined use of a MO genetic algorithm (MOGA) and a code that implements a dual boundary element (BE) formulation for the assessment of very thin road barriers idealized as single-wire configurations. The MOGA software used in this study applies an own implementation of the NSGA-II [11] algorithm, one of the references in the EMO field. In this sense, results here presented are a step forward in the direction marked by previous works developed within the SIANI institute [12-14].

Two-dimensional sound propagation hypotheses are considered, i.e., an infinite, coherent mono-frequency source of sound and a noise barrier with no geometric variation that stands on a flat plane (ground) of uniform admittance. The problem is performed in the frequency domain with the usual assumptions (Helmholtz equation): the medium (air) is modeled as homogeneous, elastic and isotropic with no viscosity, under small disturbances and initially at rest with no wind effects. Expression of the objective function to be maximized throughout the shape optimization process is written in terms of this response.