Dual mutation strategies for mixed-integer optimisation in power station design
K. Chen, Ian C. Parmee, Chris R. Gane · 2002
This paper presents the integration of evolutionary search (AS) with the design and operation of nuclear power stations. The objective is to improve the overall performance of the thermal cycle of a nuclear power plant by optimising both station design and operation using integrated evolutionary search and conventional optimisation techniques. The problem pursued is in the class of mixed-integer, non-linear constrained optimisation problems. After an initial parametric study of various adaptive search and classical optimisation techniques to determine their relative potential within a search space characterised by heavy non-linear constraints, a hybrid approach has been developed. This firstly utilises a genetic algorithm (GA) as a pre-processor to identify a feasible region within the search space before employing a dual-mutation GA strategy to search the space of mixed-integer variables. A linear programming optimisation routine then periodically searches from the best GA points with the design configuration fixed to return an optimal solution in terms of plant performance.