Abstract
The design of Pressurized Water Reactor (PWR) reload cores is a difficult combinatorial optimization problem with multiple competing objectives. This paper describes the use of a Genetic Algorithm (GA) to perform true multiobjective optimization on the PWR reload core design problem and improvements made to its performance in identifying nondominated solutions to represent the trade-off surface between competing objectives. The use of different pairing strategies for combining parents is investigated and found to produce promising results in some cases.
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Wildman, A., Parks, G. (2003). A Comparative Study of Selective Breeding Strategies in a Multiobjective Genetic Algorithm. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds) Evolutionary Multi-Criterion Optimization. EMO 2003. Lecture Notes in Computer Science, vol 2632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36970-8_30
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DOI: https://doi.org/10.1007/3-540-36970-8_30
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