1. A method for providing a cognitive network, comprising: partially solving, by at least one network node of a plurality of network nodes, a plurality of Multi-Objective Optimization (“MOO”) algorithms for a plurality of protocol stack layers using initialization parameters generated based on project requirements; monitoring, by at least said network node, a convergence behavior of said MOO algorithms to identify when solutions thereof start to converge toward Pareto-Optimal solutions; biasing, by at least said network node, a convergence of a solution trajectory for at least a first MOO algorithm of said MOO algorithms so that compatible non-dominated solutions are generated at each of said plurality of protocol stack layers; determining, by at least said network node, a Pareto Front for each of said protocol stack layers by generating all remaining solutions for said plurality of MOO algorithms; analyzing, by at least said network node, said Pareto Fronts in aggregate to develop a plurality of best overall network solutions; ranking, by at least said network node, said plurality of best overall network solutions according to a pre-defined criteria; identifying, by at least said network node, a top ranked solution for said plurality of best overall network solutions that complies with current regulatory and mission policies; computing, by at least said network node, configuration parameters for protocols of said protocol stack layers that enable implementation of said top ranked solution within said cognitive network; and dynamically re-configuring network resources of said protocol stack layers in accordance with said configuration parameters.