Multi-Objective Optimization Analysis of Economic Indicators for Nuclear Power Plant Reactor Primary Loop System Based on NHGA-NSGA-II Hybrid Algorithm Framework

Nuclear energy offers a zero-carbon solution to emission challenges, yet nuclear power plant design is constrained by spatial limitations and complex nonlinear parameter interactions. This study develops a hybrid genetic multi-objective optimization framework, NHGA-NSGA-II, by integrating refined NHGA strategies with the NSGA-II technique. Applied to a reactor primary loop system, the framework reveals a fundamental trade-off between system miniaturization (mass/volume) and passive safety (natural circulation and MDNBR). Pareto analysis indicates that Optimization Plan 3 corresponds to the most favorable representative trade-off identified under the present modeling assumptions, optimization settings, and constraint framework, achieving a 20% gain in natural circulation capacity and a 5.9% safety improvement with only a 9.2% cost increase, thereby illustrating a balanced relationship among passive safety, compactness, and economic efficiency within the current scope of the study. The proposed framework offers an effective tool for high-dimensional nonlinear optimization in nuclear engineering.