A pre-regularization scheme for the reconstruction of a spatial dependent scattering albedo using a hybrid ant colony optimization implementation

Abstract

a b s t r a c tThis work presents a regularization technique applied to an inverse radiative transfer problem formulated as a finite dimensional optimization problem and solved by a hybridization of the ant colony optimization (ACO) with the Levenberg-Marquardt method. It is considered a one-dimensional isotropically-scattering medium with finite optical thickness, space dependent scattering albedo and plane-parallel geometry. The direct radiative transfer problem models transmission of radiation through this medium by the linear version of the Boltzmann equation considering polar angle discretization and azimuthal symmetry. A discrete ordinates method combined with the finite difference method is employed to solve it. Reconstruction of the albedo profile is performed from the intensities of the polar-discretized emergent radiation acquired with external detectors, using a recently proposed regularization technique. Since smooth albedo profiles are expected, such information is used in a new generation of ants in order to perform a pre-selection of the ants. This scheme can be viewed as a kind of pre-regularization in face of the reconstructed profiles that are smooth and show good agreement with the exact solution. In addition, this scheme saves processing time as fewer candidate solutions (ants) are evaluated. Noiseless and noisy data of the emergent radiation intensities were employed in the reconstructions.

Performance analysis and feasibility study of ant colony optimization, particle swarm optimization and cuckoo search algorithms for inverse heat transfer problems

“…However, fluctuations in the estimated heat flux can be minimized further by increasing number of iterations or fine adjustment of the parameters associated with these algorithms [20,21]. In fact, pre-selection with second order Tikhonov regularization is found effective as well as efficient as number of ants (candidate solutions) to be evaluated in ACO reduces significantly [22,23]. This scheme can be easily extended to PSO and CS as well since all are population based search algorithms.…”

“…Hence, these methods are more robust. Computational time is of course a concern for these methods [16] but with ever growing computational power, parallelization [19], modifications [20][21][22] and development of hybrid methods [23][24][25], these methods can substitute http://dx.doi.org/10.1016/j.ijheatmasstransfer.2015.05.015 0017-9310/Ó 2015 Elsevier Ltd. All rights reserved. the conventional methods.…”

Solving inverse problems of radiative heat transfer and phase change in semitransparent medium by using Improved Quantum Particle Swarm Optimization

“…Recently, several heuristic algorithms were developed to solve the inverse radiation and phase change problems, such as genetic algorithm [14,15], particle swarm optimization [16,17], ant colony optimization [18,19], Artificial Neural Network [20,21], etc. Compared with traditional gradient-based methods, these heuristic algorithms have some inherent superiority [22].…”

Retrieval of Optical Constant and Particle Size Distribution of Particulate Media Using the PSO-Based Neural Network Algorithm

“…Literature survey reveals that there is no reported work about estimating of optical constants and PSDs using artificial neural network (ANN). An important advantage of this method in comparison with other inverse heat transfer modeling approaches, such as the Gauss-Newton method [23], conjugate gradient method [24], genetic algorithm [25], and ant colony optimization [26,27], to name a few, is that detailed knowledge of the geometrical and thermal properties of the system (such as wall conductivity and emissivity) is not necessary [28,29]. In many cases, the measurement of such physical properties is extremely difficult or even impossible.…”