A Modified Multiobjective Self-Adaptive Differential Evolution Algorithm and Its Application on Optimization Design of the Nuclear Power System

Yunjia Yang,Dan Zhang,Lin Xian,Li Zhu,Zhifang Qiu,Hongsheng Yuan,Shinian Peng

doi:10.1155/2019/1041486

Abstract

A modified multiobjective self-adaptive differential evolution algorithm (MMOSADE) is presented in this paper to improve the accuracy of multiobjective optimization design in the nuclear power system. The performance of the MMOSADE is tested by the ZDT test function set and compared with classical evolutionary algorithms. The results indicate that MMOSADE has a better performance in convergence and diversity. Based on the MMOSADE, a multiobjective optimization design platform for the nuclear power system is proposed, and the application of which is carried out. The evaluation program of the PRHR-HX in AP1000 is developed, and its reliability is verified. The optimal design schemes of PHHR-HX are obtained by utilizing the multiobjective optimization design platform. The results show that the optimal design schemes can envelop the prototype design scheme. This conclusion proves that the optimization design platform proposed in this paper is effective and feasible.

Highlights

Taking the nuclear safety system as an example, the current design method of nuclear power system mainly adopts the linear iteration mode of trial-evaluation-correction
In response to the above issues, Chen et al [4,5,6] carried out a series of studies on multiobjective optimization problems (MOP) of nuclear power systems based on the nondominated sorting genetic algorithm-II (NSGA-II)
modified multiobjective self-adaptive differential evolution algorithm (MMOSADE) generates new design schemes based on the evaluation indicators

Summary

Introduction

Taking the nuclear safety system as an example, the current design method of nuclear power system mainly adopts the linear iteration mode of trial-evaluation-correction. The multiobjective optimization design of nuclear power plants is mainly based on predefining weighting factors for different optimal targets to translate the multiobjective optimization problems (MOP) into single-objective optimization problems [1,2,3]. In response to the above issues, Chen et al [4,5,6] carried out a series of studies on MOP of nuclear power systems based on the nondominated sorting genetic algorithm-II (NSGA-II). This algorithm applies the concept of “Pareto optimal solution” [7], which can directly deals with MOP and obtain all relatively optimal solutions that satisfy the design requirements

Methods

Results

Conclusion