Quantum behaved Particle Swarm Optimization with Differential Mutation operator applied to WWER-1000 in-core fuel management optimization
作者： Mostafa Jamalipour , Reza Sayareh , Morteza Gharib , Farrokh Khoshahval , Mahmood Reza Karimi
DOI: 10.1016/J.ANUCENE.2012.11.008
关键词:
摘要: 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 (PSO) have shown good performance on in-core management optimization (ICFMO). The objective of this is to show that QPSO-DM performs very well comparable PSO (QPSO). Most the strategies ICFMO are based maximizing multiplication factor (keff) increase cycle length minimizing power peaking (Pq) in order improve integrity. PSO, QPSO been implemented fulfill these requirements first operating Bushehr Nuclear Power Plant (BNPP). results better than others. A program has written MATLAB map loading pattern optimization. WIMS CITATION used simulate reactor neutronic calculations.
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