A Distributed Intelligent Agent Platform for Genetic Optimization in CEM: Applications in a Quasi-Point Matching Method

The use of genetic algorithms in computational electromagnetics (CEM) has proved a fruitful, but resource demanding optimization method with numerous practical applications. This paper introduces a parallel distributed computing framework based on the intelligent agent technology that is capable of handling the genetic optimization for diverse CEM problems. The platform core component is the genetic search agent (GSA), a collaborative computational entity that communicates with its peers in order to carry out a genetic optimization scheme. The platform can interface with foreign codes transparently due to its flexible code loading mechanisms, specially designed for CEM applications. The framework is applied to the optimization of a quasi-point matching method with fictitious sources (QPM-FS). The analysis comprises two case studies, involving electromagnetic scattering by: i) a two-layered cylinder; and ii) a cylinder buried in a two-layered earth medium, a problem of practical use in subsurface imaging. The performance results indicate that the sophisticated distribution mechanisms achieve high speed-up, while physically intuitive conclusions are obtained from the genetic optimization of the developed QPM-FS method