Optimization of Reliability of Network of Given Connectivity using Genetic Algorithm

TL;DR

This paper presents a genetic algorithm-based numerical method to optimize the reliability of connected networks with fixed nodes and links, demonstrating efficiency and the tendency for symmetrical structures in optimal solutions.

Contribution

It introduces a novel application of genetic algorithms to network reliability optimization, including graph mutation and crossover operations.

Findings

Genetic algorithm efficiently finds reliable network topologies.

Optimal networks tend to exhibit high symmetry.

Method is effective for both small and large networks.

Abstract

Reliability is one of the important measures of how well the system meets its design objective, and mathematically is the probability that a system will perform satisfactorily for at least a given period of time. When the system is described by a connected network of N components (nodes) and their L connection (links), the reliability of the system becomes a difficult network design problem which solutions are of great practical interest in science and engineering. This paper discusses the numerical method of finding the most reliable network for a given N and L using genetic algorithm. For a given topology of the network, the reliability is numerically computed using adjacency matrix. For a search in the space of all possible topologies of the connected network with N nodes and L links, genetic operators such as mutation and crossover are applied to the adjacency matrix through a string representation. In the context of graphs, the mutation of strings in genetic algorithm corresponds to the rewiring of graphs, while crossover corresponds to the interchange of the sub-graphs. For small networks where the most reliable network can be found by exhaustive search, genetic algorithm is very efficient. For larger networks, our results not only demonstrate the efficiency of our algorithm, but also suggest that the most reliable network will have high symmetry.