Implementation of the enhanced ant colony system algorithm to solve reliable communication network design

Abstract

The problem of communication design has been defined as one of the problems that belong to the category of NP-hard problem, and the aim of the topological communication network design is to identify component placement locations and connectivity aspects. On the other hand, the Reliable Communication Network Design (RCND) is a popular optimization problem used for maximizing network reliability. In addition, finding an accurate calculation of RCND explains the problem of NP-hard problem. To this end, literature studies suggested various metaheuristic algorithms that have been used as approximation methods to find the best solution to this problem. Some of these algorithms belong to the Evolutionary Algorithms (EAs) category, such as Genetic Algorithms (GAs), and some belong to the Swarm Intelligence Algorithms (SIAs) category, such as Ant Colony Optimization (ACO). However, to the best of our knowledge, the Ant Colony System (ACS) algorithm, which is considered an updated version of ACO, has not yet been used to design reliability-constrained communication network topologies. Therefore, this study aims to apply the updated version of the ACS algorithm for solving RCND in small, medium, and large networks. The proposed algorithm was benchmarked against present state-of-the-art techniques that address this challenge. The research findings show that the proposed algorithm is an optimal solution for a fully connected small network size (n=6, 7, 8, and 9) and it has been achieved as an optimal solution for all not fully connected sets (n=14, 16, and 20). In each case, the results for medium-sized networks were better than the benchmark results