161
Views
3
CrossRef citations to date
0
Altmetric
Original Articles

Optimization of the Electrical Performance of Polymeric Films

, , &
Pages 464-473 | Received 25 Jun 2014, Accepted 19 Sep 2014, Published online: 13 Feb 2015
 

Abstract

In the present study, an attempt is made to optimize the electrical performance of the thin polymeric films through optimization techniques. The study is conducted in two phases: (1) laboratory experiments and (2) through numerical optimization. For laboratory analysis, thin and transparent films are prepared using polyethersulfone (PES) as host material and meta-nitroaniline (MNA) as guest materials. A set of nine film samples are prepared by the solution casting method in the laboratory using different concentrations of MNA. The electrical properties capacitance, conductance, and dissipation factor of films are measured by Aligent Impedance Analyzer. These characteristics are then optimized mathematically. For this purpose, initially single-objectives are considered for optimizing the electrical properties individually, and later a multiobjective model is considered for analyzing the properties simultaneously. The algorithms employed are metaheuristics: genetic algorithms, particle swarm optimization, differential evolution, and its variant modified differential evolution along with fmincon (a MATLAB toolbox) for single-objective optimization and multiobjective differential evolution algorithm and nondominated sorting genetic algorithm-II for multiobjective optimization.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 561.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.