The Influence of Quantum Aspect to Artificial Ant Colony on Nuclear Reload Optimization Problem pp. 69-96
Authors: (Márcio Henrique da Silva, Roberto Schirru, Universidade Federal do Rio de Janeiro, PEN/COPPE, Rio de Janeiro, Brazil)
Abstract: This chapter aims to analyze the influence of merging quantum computing concepts such as the quantum bit representation with the biological metaphor of real ants used in Ant Colony Optimization (ACO) when applied to solve the nuclear reload of Brazilian‘s pressurized water reactor (PWR) of Angra 1. ACO is a bio-inspired algorithm where the artificial agents evolve through generations by means of the biological metaphor of collective learning. It was developed to solve the traveling salesman problem (TSP), a well-known issue that consists in find the shorter path scoured by a traveler who must visit each available city only once, returning to the starter one at the end of his journey. TSP is quite similar to the nuclear reload optimization problem concerning to their complexity and to the fact that both of them are NP-complete problems where is not allowed the repetition of their elements. For this reason, ACO started to be used as optimization tool in nuclear reload problem where, according to previous researches, it has been obtaining good results.