Optimizing AEDB broadcasting protocol with parallel multi-objective cooperative coevolutionary NSGA-II

Abstract

Due to the highly unpredictable topology of ad hoc networks, most of the existing communication protocols rely on different thresholds for adapting their behavior to the environment. Good performance is required under any circumstances. Therefore, finding the optimal configuration for those protocols and algorithms implemented in these networks is a complex task. We propose in this work to automatically fine tune the AEDB broadcasting protocol for MANETs thanks to the use of cooperative coevolutionary multi-objective evolutionary algorithms. AEDB is an advanced adaptive protocol based on the Distance Based broadcasting algorithm that acts differently according to local information to minimize the energy and network use, while maximizing the coverage of the broadcasting process. In this work, it will be fine tuned using multi-objective techniques in terms of the conflicting objectives: coverage, energy and network resources, subject to a broadcast time constraint. Because of the few parameters of AEDB, we defined new versions of the problem in which variables are discretized into bit-strings, making it more suitable for cooperative coevolutionary algorithms. Two versions of the proposed method are evaluated and compared versus the original NSGA-II, providing highly accurate tradeoff configurations in shorter execution times.