Real coded multi-objective nested accelerating genetic algorithm based on complex system evolutionary optimization
Qiang Fu, Kai You Wang · Systems Engineering - Theory & Practice · 2012
The research of complex system science has became an very essential part of the system science. However,due to it's multi-level,nonlinear,and coupled with a large number of random uncertainties in the evolution of simulation work to the system caused immense difficulties.On this background,the real coded multi-objective nested accelerating genetic algorithm(RMONAGA) has been proposed to evolve and simulate the complex system.Based on the similarity between the features of system and characteristics of this algorithm,the problem of interaction of different levels and the adaptive conflict between different sectors has been solved by using the nested and multi-objective method.The method is applied to do the research of regional agricultural soil and water resources complex adaptive system optimization of Sanjiang Plain.The result shows that this method is scientifically rational,in order to provide some help in the field of modeling and simulation when CAS theory have been applied.