Fuel management optimization using genetic algorithms and expert knowledge

被引：0

作者：

Pennsylvania State Univ, United States ^{[1
]}

机构：

来源：

Nucl Sci Eng | / 1卷 / 188-196期

关键词：

Codes (symbols) - Constraint theory - Encoding (symbols) - Functions - Genetic algorithms - Knowledge based systems - Pressurized water reactors - Problem solving - Reactor cores;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

The CIGARO fuel management optimization code based on genetic algorithms is described and tested. The test problem optimized the core lifetime for a pressurized water reactor with a penalty function constraint on the peak normalized power. A bit-string genotype encoded the loading patterns, and genotype bias was reduced with additional bits. Expert knowledge about fuel management was incorporated into the genetic algorithm. Regional crossover exchanged physically adjacent fuel assemblies and improved the optimization slightly. Biasing the initial population toward a known priority table significantly improved the optimization.

引用