Research On The Synergy Theory, Method And Application Of Complex Adaptive System

The theory of complex adaptive system (CAS) is a focus of the research of complex system in the past decade, which provide us a novel method and a new direction of complex system research. As the research about CAS, the synergy theory, method of CAS and its application in energy complex system is studied in this paper. We begin by introducing the theory and method of CAS that includes the conception of complex adaptive system, adaptive agent, agent model expressed by stimulus-response model and the Echo model used for CAS description. The principle, modeling and simulation process of the software platform Swarm, which is the tool of CAS simulation, is introduced. As a generalization, the method of CAS research is reviewed. In the end of chapter 2, thedeficiency of description and application and the future research direction of CAS is presented. The conception of system co-evolution is presented in chapter 3. The phenomena of biology system is popular, also there are many reflection of co-evolution in soci-economy system. The definition and examples of biology system co-evolution is generalized. Inspired from the definition and classifying of individual.s relationship in biology system,themechanism of system co-evolution is described. Based on it, definition, classifying of system co-evolution and thehierarchy model of agent, population and system is presented. In chapter 4, the modeling of CAS is discussed. The basic characteristic of CAS is that it is composed of adaptive agent. We describe the mechanism and mathematical model of CAS based on theory of biology system. After the analysis of CAS and Echo model, modeling of CAS is discussed based on the theory and method of Multi-Agent System (MAS). We give a comparison of Equation-based modeling and Agent-based modeling and present a open, dynamic and evolving modeling frame of CAS based on co-evolution and theory of agent system. The co-evolution of system is studied detailedly in chapter 5. First the adaptation of biology system and Genetic Algorithm (GA) is reviewed briefly. We discussed the cooperative co-evolution and competitive co-evolution. In cooperative co-evolution the fitness of an individualis determined by a series of collaborations withindividuals from other population. In competitiveco-evolution the fitness of an individualis determined by a series of competitions with individuals from other population. As a method of cooperative co-evolution, we present the cooperative co-evolution genetic algorithm, including the conception, model, algorithm and application. A population density based competitive coevolution algorithm is studied. A co-evolution based on evolutionary game theory (EGT) is analyzed, in which apply the multi-population asymmetric game to the study of individual behavior. II As the application case, the energy complex system of China is studied in chapter 6 based on the theory and method discussed in advanced. Thesituation of China energy system is generalized first. Seeinginto thestructure and evolution characteristic of energy system, it can be regard as a complex...