Study of Nuclear Reactor Reload Using Different Approaches of Quantum Inspired Algorithms

摘要

The purpose of this article is to show the performance of different approaches of quantum-inspired algorithms as optimization tool of Nuclear Reactor Reload of Brazilian Nuclear Power Plant. Nuclear Reactor Reload is a classical problem in Nuclear Engineering that has been studied for more than 40 years that focus on the economics and safety of the Nuclear Power Plant. The main goal of this article is to show the performance of Quantum Delta-Potential Well Based Particle Swarm Optimization Algorithm to solve the Nuclear Reactor Reload compared with its classical counterpart Particle Swarm Optimization with Random Keys method. Furthermore, others quantum inspired algorithms are also used to demonstrate the feasibility of quantum inspired algorithms to solve cycle 7 of Brazilian Nuclear Power Plant Angra 1. The results show that Quantum Delta-Potential Well Based Particle Swarm Optimization Algorithm found the best result with less computational effort than its classical counterpart. Besides shows that quantum inspired algorithm are well situated among the best alternatives for dealing with optimization problems that number of evaluations is crucial due to the high computational cost of the evaluations, such as Nuclear Reactor Reload.