A hybrid nature-inspired optimizer for wireless mesh networks design

Existing approaches for optimal planning of wireless mesh networks (WMNs) deployment revolves around the deployment cost as the pivotal concept to optimize. In this paper, we adopt a new approach to optimize the planning of WMNs that guarantees an acceptable level of network performance prior to its deployment. It is a simultaneous optimization process of network deployment cost and network throughput objectives while taking into account all the parameters that have a significant impact on the network efficiency. We propose three multi-objective models for WMN planning problem, namely Load-Balanced Model, Interference Model, and Flow-Capacity Model. We devise an evolutionary swarm-based algorithm that is a hybrid combination of multi-objective Particle Swarm Optimization (MOPSO) and Genetic Algorithms (GAs) to solve the three models. We use realistic network sizes (up to 100 mesh nodes) to perform a thorough comparative experimental study on these three instance models with different key-parameter settings. Finally, we use the network simulator OMNET++ to evaluate the three models in terms of the actual performance (network throughput). The results presented in this paper show that Load-Balanced Model totally supersedes the Flow-Capacity Model and performs better than the Interference Model.