New analysis of the optimization of electromagnetic shielding properties using conducting polymers and a multi‐objective approach

Abstract

Recently, an approach was proposed to optimize multi-layer shields of polyaniline-polyurethane (PAni/PU) conducting composites in the microwave band. Though by this method shields for different applications can be obtained which are light-weight and offer a low percolation threshold, the full potential of the design process could not be tapped since the underlying optimization problem includes only one objective. In this work we go one step beyond and re-formulate the design problem as a multi-objective optimization problem (MOP). To be more precise, we involve simultaneously the shielding efficiency as well as the weight and the cost of the material-i.e. all the requirements for modern shielding materials-within the optimization process. After having stated the model we present two possible ways to approximate the solution set-the so-called Pareto setand address the related and important decision-making problem. All steps are demonstrated on a particular three-layered composite in order to show the applicability of the novel approach.

Mechanically robust conductive carbon clusters confined ethylene methyl acrylate–based flexible composites for superior shielding effectiveness

“…For effective shielding, material should contain either mobile charge carriers or electric and magnetic dipoles, which can interact with electric and magnetic vectors of electromagnetic radiation to resist electromagnetic energy from any external sources . Since a long period of time, metals have been used as EMI shielding materials but upcoming trend shifts towards polymer nanocomposites because of their light weight, noncorrosive nature and low price . Polymer nanocomposites fabricated with carbonaceous materials such as graphite, single and multiwall carbon nanotube (CNT), carbon fiber, and carbon black have been widely used as EMI shielding materials .…”

A software framework based on a conceptual unified model for evolutionary multiobjective optimization: ParadisEO-MOEO

“…Indeed, various academic MOPs have been tackled, including continuous test functions such as the ZDT and DTLZ functions family (Deb et al, 2005b) or the Schaffer problem (Schaffer, 1985), scheduling problems including permutation flow-shop scheduling (Liefooghe et al, 2007a), routing problems such as the multiobjective traveling salesman problem and the bi-objective ring star problem (Liefooghe et al, 2010). Moreover, ParadisEO-MOEO has been successfully applied to solve real-world applications in structural biology (Boisson et al, 2008), feature selection in cancer classification (Talbi et al, 2008), materials design in chemistry (Schuetze et al, 2008), portfolio optimization, etc. Note that the problem-related part of some aforementioned applications is freely available as contributions on the ParadisEO website (http://paradiseo.gforge.inria.fr), together with a detailed documentation and some tutorial lessons.…”

ParadisEO-MOEO: A Software Framework for Evolutionary Multi-Objective Optimization

“…Indeed, various academic MOPs have been tackled within ParadisEO-MOEO, including continuous test functions (like the ZDT and DTLZ functions family defined in [16]), scheduling problems (permutation flow-shop scheduling problem [32]), routing problems (multi-objective traveling salesman problem, bi-objective ring star problem [34]), etc. Moreover, it has been successfully employed to solve real-world applications in structural biology [9], feature selection in cancer classification [44], data-mining [29], materials design in chemistry [40], etc. Besides, a detailed documentation as well as some tutorial lessons and problem-specific implementations are freely available on the ParadisEO website 6 .…”