2005/02/02 by Mark Fleischer, Fleischer, Mark
Computer Science · Decision Sciences · Physics and Astronomy · Social Sciences · #Adaptation and Self-Organizing Systems (nlin.AO) #Complex Systems and Decision Making #Evolutionary Algorithms and Applications #Evolutionary Game Theory and Cooperation #FOS: Physical sciences #nlin.AO
paper · pdf · doi:10.48550/arxiv.nlin/0502003
13 pages, 4 graphics, Conference on Swarming: Network Enabled C4ISR, January 13-14, 2003 McLean, Virginia
arxiv created 2005/02/02 · openalex publication_date 2005/02/02 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Swarm Intelligence (SI) is a relatively new paradigm being applied in a host of research settings to improve the management and control of large numbers of interacting entities such as communication, computer and sensor networks, satellite constellations and more. Attempts to take advantage of this paradigm and mimic the behavior of insect swarms however often lead to many different implementations of SI. The rather vague notions of what constitutes self-organized behavior lead to rather ad hoc approaches that make it difficult to ascertain just what SI is, assess its true potential and more fully take advantage of it. This article provides a set of general principles for SI research and development. A precise definition of self-organized behavior is described and provides the basis for a more axiomatic and logical approach to research and development as opposed to the more 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 symmetry relationships and scale invariance where Pareto optimality is preserved under changes in system states. This provides a mathematical way to describe efficient tradeoffs of efficiency between different scales and further, mathematically captures the notion of the graceful degradation of performance so often sought in complex systems.