Genetic algorithm (GA) has been widely applied in optimal design of nuclear power components. Simple genetic algorithm (SGA) has the defects of poor convergence accuracy and easily falling into the local optimum when dealing with nonlinear constraint optimization problem. To overcome these defects, an improved genetic algorithm named dual-adaptive niched genetic algorithm (DANGA) is designed in this work. The new algorithm adopts niche technique to enhance global search ability, which utilizes a sharing function to maintain population diversity. Dual-adaptation technique is developed to improve the global and local search capability at the same time. Furthermore, a new reconstitution operator is applied to the DANGA to handle the constraint conditions, which can avoid the difficulty of selecting punishment parameter when using the penalty function method. The performance of new algorithm is evaluated by optimizing the benchmark function. The volume optimization of the Qinshan I steam generator and the weight optimization of Qinshan I condenser, taking thermal-hydraulic and geometric constraints into consideration, is carried out by adopting the DANGA. The result of benchmark function test shows that the new algorithm is more effective than some traditional genetic algorithms. The optimization design shows obvious validity and can provide guidance for real engineering design.

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