A Metropolis algorithm combined with Nelder–Mead Simplex applied to nuclear reactor core design

Abstract

A hybridization of the recently introduced Particle Collision Algorithm (PCA) and the Nelder–Mead Simplex algorithm is introduced and applied to a core design optimization problem which was previously attacked by other metaheuristics. The optimization problem consists in adjusting several reactor cell parameters, such as dimensions, enrichment and materials, in order to minimize the average peak-factor in a three-enrichment-zone reactor, considering restrictions on the average thermal flux, criticality and sub-moderation. The new metaheuristic performs better than the genetic algorithm, particle swarm optimization, and the Metropolis algorithms PCA and the Great Deluge Algorithm, thus demonstrating its potential for other applications.