Table of Contents
Advances in Artificial Intelligence
Volume 2017 (2017), Article ID 3497652, 22 pages
https://doi.org/10.1155/2017/3497652
Research Article

Selection and Configuration of Sorption Isotherm Models in Soils Using Artificial Bees Guided by the Particle Swarm

Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam 781039, India

Correspondence should be addressed to Tadikonda Venkata Bharat; ni.tenre.gtii@bvt

Received 23 March 2016; Revised 10 October 2016; Accepted 22 November 2016; Published 18 January 2017

Academic Editor: David Glass

Copyright © 2017 Tadikonda Venkata Bharat. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A precise estimation of isotherm model parameters and selection of isotherms from the measured data are essential for the fate and transport of toxic contaminants in the environment. Nonlinear least-square techniques are widely used for fitting the isotherm model on the experimental data. However, such conventional techniques pose several limitations in the parameter estimation and the choice of appropriate isotherm model as shown in this paper. It is demonstrated in the present work that the classical deterministic techniques are sensitive to the initial guess and thus the performance is impeded by the presence of local optima. A novel solver based on modified artificial bee-colony (MABC) algorithm is proposed in this work for the selection and configuration of appropriate sorption isotherms. The performance of the proposed solver is compared with the other three solvers based on swarm intelligence for model parameter estimation using measured data from 21 soils. Performance comparison of developed solvers on the measured data reveals that the proposed solver demonstrates excellent convergence capabilities due to the superior exploration-exploitation abilities. The estimated solutions by the proposed solver are almost identical to the mean fitness values obtained over 20 independent runs. The advantages of the proposed solver are presented.