Quantum behaved Particle Swarm Optimization with Differential Mutation operator applied to WWER-1000 in-core fuel management optimization

This paper presents a new method using Quantum Particle Swarm Optimization with Differential Mutation operator (QPSO-DM) for optimizing WWER-1000 core fuel management. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have shown good performance on in-core fuel management optimization (ICFMO). The objective of this paper is to show that QPSO-DM performs very well and is comparable to PSO and Quantum Particle Swarm Optimization (QPSO). Most of the strategies for ICFMO are based on maximizing multiplication factor (keff) to increase cycle length and minimizing power peaking factor (Pq) in order to improve fuel integrity. PSO, QPSO and QPSO-DM have been implemented to fulfill these requirements for the first operating cycle of WWER-1000 Bushehr Nuclear Power Plant (BNPP). The results show that QPSO-DM performs better than the others. A program has been written in MATLAB to map PSO, QPSO and QPSO-DM for loading pattern optimization. WIMS and CITATION have been used to simulate reactor core for neutronic calculations.

► A new method called QPSO-DM is applied to BNPP in-core fuel management optimization.
► It is found that QPSO-DM performs better than PSO and QPSO.
► This method provides a permissible arrangement for optimum loading pattern.