Immune algorithms-based approach for redundant reliability problems with multiple component choices

Authors:
Ta-Cheng Chen;Peng-Sheng You
Affiliations:
Department of Information Management, National Formosa University, Huwei, Yulin 632, Taiwan;Graduate Institute of Transportation and Logistics, National Chia-Yi University, Chia-Yi 600, Taiwan
Venue:
Computers in Industry - Special issue: Application of genetics algorithms in industry
Year:
2005

Citing 6
Cited 4

The immune system, adaptation, and machine learning

Physica D
Introduction to operations research, 4th ed.

Introduction to operations research, 4th ed.
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)

Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Penalty guided genetic search for reliability design optimization

Computers and Industrial Engineering
Artificial Immune Systems: A New Computational Intelligence Paradigm

Artificial Immune Systems: A New Computational Intelligence Paradigm
A linear approximation for redundant reliability problems with multiple component choices

Computers and Industrial Engineering

Using neural networks and immune algorithms to find the optimal parameters for an IC wire bonding process

Expert Systems with Applications: An International Journal
Efficient combined immune-decomposition algorithm for optimal buffer allocation in production lines for throughput and profit maximization

Computers and Operations Research
Artificial immune systems for assembly sequence planning exploration

Engineering Applications of Artificial Intelligence
Multi-objective redundancy allocation optimization using a variable neighborhood search algorithm

Journal of Heuristics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper considers the series-parallel redundant reliability problems in which both the multiple component choices of each subsystem and the redundancy levels of every selected component are to be decided simultaneously so as to maximize the system reliability. The reliability design optimization problem has been studied in the literature for decades, usually using mathematical programming or heuristic optimization approaches. The difficulties encountered for both methodologies are the number of constraints and the difficulty of satisfying the constraints. A penalty-guided immune algorithms-based approach is presented for solving such integer nonlinear redundant reliability design problem. The results obtained by using immune algorithms-based approach are compared with the results obtained from 33 test problems from the literature that dominate the previously mentioned solution techniques. As reported, solutions obtained by the proposed method are better than or as well as the previously best-known solutions.