Swarm Intelligence (SI) is a relatively new paradigm being appliedin a host of research settings to improve the management andcontrol of large numbers of interacting entities such ascommunication, computer and sensor networks, satelliteconstellations and more. Attempts to take advantage of thisparadigm and mimic the behavior of insect swarms however oftenlead to many different implementations of SI. The rather vaguenotions of what constitutes self-organized behavior lead to ratherad hoc approaches that make it difficult to ascertain justwhat SI is, assess its true potential and more fully takeadvantage of it. This article provides a set of general principles for SI researchand development. A precise definition of {em self-organizedbehavior} is described and provides the basis for a more axiomaticand logical approach to research and development as opposed to themore prevalent ad hoc approach in using SI concepts. The concept of Pareto optimality is utilized to capture the notions of efficiency and adaptability. A new concept, Scale Invariant Pareto Optimality, is described and entails symmetryrelationships and scale invariance where Pareto optimality ispreserved under changes in system states. This provides amathematical way to describe efficient tradeoffs ofefficiency between different scales and further, mathematicallycaptures the notion of the graceful degradation ofperformance so often sought in complex systems.