Particle swarm evolutionary computation-based framework for optimizing the risk and cost of low-demand systems of nuclear power plants. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Ge, Daochuan; Chen, Shanqi; Wang, Zhen; Yang, Yanhua
• Is Part Of: Journal of nuclear science and technology. Volume 55:Issue 1(2018)
• Page Start: 19
• Page End: 28
• Publication Date: 2018-01-02
• Subjects: Nuclear power plant -- surveillance test -- multi-objective optimization -- particle swarm optimization -- mixed integers -- multi-swarm co-evolutionary; Nuclear engineering -- Periodicals; Nuclear physics -- Periodicals; Nuclear energy -- Periodicals; 621.4805
• DOI: 10.1080/00223131.2017.1383208
• Languages: English
• ISSNs: 0022-3131

Abstract

ABSTRACT: In this paper, an adapted multi-objective multi-swarm co-evolutionary particle swarm optimization (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-evolutionary strategy 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 effectiveness 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.