2026-10-12T00:34:45Z https://www.scidb.cn/oai

10.57760/sciencedb.05700 2022-11-27T21:57:54Z

2022-11-27 Particle swarm evolutionary computation-based framework for optimizing the risk and cost of low-demand systems of nuclear power plants doi:10.57760/sciencedb.05700 en In this paper, an adapted multi-objective multi-swarm co-evolutionary particle swarm optimiza tion (PSO) framework is developed to simultaneously optimize the risk and cost of low-demand systems of nuclear power plants (NPPs). In the built framework, multi-swarm co-evolutionarystrategy is introduced to handle the fitness assignment puzzle of multi-objective optimization problems. Besides, to deal with the mixed-integer problem of the decision variables vector, a sub-interval covering-based nearest boundary method is also adopted. To illustrate the effective ness and efficiencies of the proposed method, a typical high-pressurized injection system (HPIS) is analyzed. The results indicate that, compared with the classic non-dominated sorting genetic algorithm (NSGA)-II approach, the proposed method is more simple and easier to be convergent, besides, of which the Pareto front is better distributed Nuclear power plant; surveillance test; multi-objective optimization; particle swarm optimization; mixed integers; multi-swarm co-evolutionary Daochuan Ge Shanqi Chen Zhen Wang Yanhua Yang PUBLIC https://creativecommons.org/licenses/by-nc/4.0/ dataset http://www.doi.org/10.1080/00223131.2017.1383208 Science Data Bank