In this paper, we present new models and algorithms for control and optimization of a class of next generation communication networks: Hierarchical Heterogeneous Wireless Networks (HHWNs), under real-world physical constraints. Two biology-inspired techniques, a Flocking Algorithm (FA) and a Particle Swarm Optimizer (PSO), are investigated in this context. Our model is based on the control framework at the physical layer presented previously by the authors. We first develop a nonconvex mathematical model for HHWNs. Second, we propose a new FA for self-organization and control of the backbone nodes in an HHWN by collecting local information from end users. Third, we employ PSO, a widely used artificial intelligence algorithm, to directly optimize the HHWN by collecting global information from the entire system. A comprehensive evaluation measurement during the optimization process is developed. In addition, the relationship between HHWN and FA and the comparison of FA and PSO are discussed, respectively. Our novel framework is examined in various dynamic scenarios. Experimental results demonstrate that FA and PSO both outperform current algorithms for the self-organization and optimization of HHWNs while showing different characteristics with respect to convergence speed and quality of solutions.
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