Parallel multi-objective metaheuristics for smart communications in vehicular networks

TL;DR

This paper presents parallel multi-objective soft computing algorithms that optimize routing protocol configurations in vehicular networks, significantly improving performance and computational efficiency over existing methods.

Contribution

Introduces novel parallel multi-objective algorithms based on evolutionary and swarm intelligence for optimizing vehicular network routing protocols.

Findings

Optimized configurations outperform state-of-the-art methods.

Parallel algorithms achieve over 87% computational efficiency.

Framework effectively enhances vehicular communication performance.

Abstract

This article analyzes the use of two parallel multi-objective soft computing algorithms to automatically search for high-quality settings of the Ad hoc On Demand Vector routing protocol for vehicular networks. These methods are based on an evolutionary algorithm and on a swarm intelligence approach. The experimental analysis demonstrates that the configurations computed by our optimization algorithms outperform other state-of-the-art optimized ones. In turn, the computational efficiency achieved by all the parallel versions is greater than 87 %. Therefore, the line of work presented in this article represents an efficient framework to improve vehicular communications.