A POPULATIONAL PARTICLE COLLISION ALGORITHM APPLIED TO A NUCLEAR REACTOR CORE DESIGN OPTIMIZATION

The Particle Collision Algorithm (PCA) is a recently introduced metaheuristic conceptually similar to Simulated Annealing, without, though, the necessity of estimating the free parameters as in the latter algorithm. It is loosely inspired by the physics of nuclear particle collision reactions, particularly scattering and absorption. A “particle” that reaches a promising area of the search space is “absorbed”, while one that hits a low-fitness region is “scattered”. PCA is also a Metropolis algorithm, as a solution worse than the currently best may be accepted with a certain probability. In this article, we introduce a populational version of the Particle Collision Algorithm, the Populational PCA (PopPCA), which is a hybridization of the PCA and the genetic algorithm (GA). At the end of a generation, the particles reproduce and the fittest individuals survive. We apply the new algorithm to an optimization problem that consists in adjusting several reactor cell parameters, such as dimensions, enrichment and materials, in order to minimize the average peakfactor in a three-enrichment-zone reactor, considering restrictions on the average thermal flux, criticality and sub-moderation. The populational PCA is compared to other metaheuristics previously applied to the problem and shows to perform better than them, thus demonstrating its potential for other applications and further development.