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Sandia and Aeva evaluate intrusion detection system
Sandia National Laboratories is collaborating with the California-based Aeva Technologies, a developer of next-generation sensing and perception systems, on the development of technology for strengthening security at U.S. nuclear reactor sites.
G. T. Parks
Nuclear Science and Engineering | Volume 124 | Number 1 | September 1996 | Pages 178-187
Technical Paper | doi.org/10.13182/NSE96-A24233
Articles are hosted by Taylor and Francis Online.
The design of pressurized water reactor reload cores is not only a formidable optimization problem but also, in many instances, a multiobjective problem. A genetic algorithm (GA) designed to perform true multiobjective optimization on such problems is described. Genetic algorithms simulate natural evolution. They differ from most optimization techniques by searching from one group of solutions to another, rather than from one solution to another. New solutions are generated by breeding from existing solutions. By selecting better (in a multiobjective sense) solutions as parents more often, the population can be evolved to reveal the trade-off surf ace between the competing objectives. An example illustrating the effectiveness of this novel method is presented and analyzed. It is found that in solving a reload design problem the algorithm evaluates a similar number of loading patterns to other state-of-the-art methods, but in the process reveals much more information about the nature of the problem being solved. The actual computational cost incurred depends on the core simulator used; the GA itself is code independent.