Open AccessIterative PSO Algorithms for RAP Problems with k-out-of-n:G Subsystems and Mixing of Components and Changing k Values
Author(s) -
LI Dong-kui,
Wulantuya,
Yanlong Zhu,
Xuebao Li,
Liiping Yang,
Yuqin Xia
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1828/1/012156
Subject(s) - particle swarm optimization , mixing (physics) , algorithm , inertia , basis (linear algebra) , swarm behaviour , component (thermodynamics) , transformation (genetics) , computer science , value (mathematics) , mathematics , mathematical optimization , iterative method , statistics , geometry , biochemistry , chemistry , physics , quantum mechanics , classical mechanics , gene , thermodynamics
The subsystem is k-out-of- n : G configuration, and the subsystem has component mixing (that is, subsystem components can be selected from different kinds of components), and the minimum number of normal working elements (k value) that can be selected as well as can change RAP problem. On the basis of giving a solution construction method and generating a new solution by symmetric bit transformation technology, an iterative DPSO (discrete particle swarm optimization) algorithm with fixed-compression coefficient and dynamic inertia weight is designed to solve the model. The calculate results indicate that the algorithm can effectively give the optimal solution for each test case (which is consistent with the existing optimal results in the literature). PSO algorithm can effectively solve the RAP problem of subsystems with mixed components and varying k values. When the size of subsystems (k-out-of- n : G) is large, and the solution of the model is limited by microcomputers.