Recognition problems for special classes of polynomials in 0-1 variables
Mathematical Programming: Series A and B
An Approach to the Reliability Optimization of Software with Redundancy
IEEE Transactions on Software Engineering
Optimization Models for Reliability of Modular Software Systems
IEEE Transactions on Software Engineering - Special issue on software reliability
Penalty guided genetic search for reliability design optimization
Computers and Industrial Engineering
The ant colony optimization meta-heuristic
New ideas in optimization
Cross-entropy and rare events for maximal cut and partition problems
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue: Rare event simulation
Tabu Search
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A linear approximation for redundant reliability problems with multiple component choices
Computers and Industrial Engineering
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics)
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
An efficient heuristic for series-parallel redundant reliability problems
Computers and Operations Research
Short communication: An effective global harmony search algorithm for reliability problems
Expert Systems with Applications: An International Journal
Mathematics and Computers in Simulation
| Hi-index | 0.00 |
The Cross Entropy method has recently been applied to combinatorial optimization problems with promising results. This paper proposes a Cross Entropy based algorithm for reliability optimization of complex systems, where one wants to maximize the reliability of a system through optimal allocation of redundant components while respecting a set of budget constraints. We illustrate the effectiveness of the proposed algorithm on two classes of problems, software system reliability optimization and complex network reliability optimization, by testing it on instances from the literature as well as on randomly generated large scale instances. Furthermore, we show how a Cross Entropy-based algorithm can be fine-tuned by using a training scheme based upon the Response Surface Methodology. Computational results show the effectiveness as well as the robustness of the algorithm on different classes of problems.