Improvement of Network Reliability by Hybridization of the Penalty Technique Based on Metaheuristic Algorithms

被引:0
|
作者
Fadhil R.A. [1 ]
Hassan Z.A.H. [1 ]
机构
[1] Department of Mathematics, College of Education, Pure Sciences University of Babylon
关键词
dwarf mongoose optimization algorithm; honey badger algorithm; Network reliability; penalty method;
D O I
10.52866/ijcsm.2024.05.01.007
中图分类号
学科分类号
摘要
In this study, a network created by converting the Petri net into a network for shutdown system, by calculating a network’s nonlinear reliability polynomial by splitting the network into two, each having a source node and an terminal node. This the procedure converts the system into two parallel-series block diagrams that are connected in a series. The metaheuristic algorithm using the honey badger algorithm (HBA) and Dwarf Mongoose Optimization Algorithm (DMO) have been used to improve the problem for the shutdown network hybridizing these algorithms employing the penalty method, we will obtained the (PFMHBA, PFMDMO) algorithms. Thereafter, we compared the results of the use of the hybridization technique with the results of the process that does not use this technique. The objective of this comparison was to ascertain whether the use of hybridization makes the results increasing reliable, reducing cost, and shortening the duration of implementation. © 2024 College of Education, Al-Iraqia University. All rights reserved.
引用
收藏
页码:99 / 111
页数:12
相关论文
共 50 条
  • [1] Hybridization of Metaheuristic and Population-Based Algorithms with Neural Network Learning for Function Approximation
    Chen, Zhen-Yao
    [J]. ADVANCES IN COMPUTATIONAL COLLECTIVE INTELLIGENCE (ICCCI 2021), 2021, 1463 : 45 - 56
  • [2] Application of metaheuristic algorithms to the improvement of the MyCiTi BRT network in Cape Town
    Nnene, O. A.
    Zuidgeest, M. H. P.
    Beukes, E. A.
    [J]. JOURNAL OF THE SOUTH AFRICAN INSTITUTION OF CIVIL ENGINEERING, 2017, 59 (04) : 56 - 63
  • [3] Optimal Control Implementation with Terminal Penalty Using Metaheuristic Algorithms
    Minzu, Viorel
    [J]. AUTOMATION, 2020, 1 (01): : 48 - 65
  • [4] Optimal Power Allocation Based on Metaheuristic Algorithms in Wireless Network
    Sun, Qiushi
    Wu, Haitao
    Petrosian, Ovanes
    [J]. MATHEMATICS, 2022, 10 (18)
  • [5] Metaheuristic Algorithms for Convolution Neural Network
    Rere, L. M. Rasdi
    Fanany, Mohamad Ivan
    Arymurthy, Aniati Murni
    [J]. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2016, 2016
  • [6] Performance Analysis of Metaheuristic Technique Based Decoding Algorithms for Block Codes
    Kolisetty, Harish
    Vadlamudi, Sai Vamsi Krishna
    Chennam, Vamsi Krishna
    Raju, Reddy R.
    Reddy, Bhargav Anand S.
    [J]. 2017 INTERNATIONAL CONFERENCE ON COMMUNICATION AND SIGNAL PROCESSING (ICCSP), 2017, : 70 - 75
  • [7] A Comparative Study of Metaheuristic Algorithms for Reliability-Based Design Optimization Problems
    Zeng Meng
    Gang Li
    Xuan Wang
    Sadiq M. Sait
    Ali Rıza Yıldız
    [J]. Archives of Computational Methods in Engineering, 2021, 28 : 1853 - 1869
  • [8] A Comparative Study of Metaheuristic Algorithms for Reliability-Based Design Optimization Problems
    Meng, Zeng
    Li, Gang
    Wang, Xuan
    Sait, Sadiq M.
    Yildiz, Ali Riza
    [J]. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING, 2021, 28 (03) : 1853 - 1869
  • [9] A new framework for reliability-based design optimization using metaheuristic algorithms
    Kaveh, Ali
    Zaerreza, Ataollah
    [J]. STRUCTURES, 2022, 38 : 1210 - 1225
  • [10] Performance Improvement of Edge Expansion Technique for BDD-based Network Reliability Analysis
    Chen, Ronggen
    Mo, Yuchang
    Pan, Zhusheng
    [J]. JOURNAL OF COMPUTERS, 2013, 8 (09) : 2190 - 2196