A multi-objective HSA of nonlinnear system modeling for hot skip-passing

A multi-objective heuristic searching approach (MoHSA) for nonlinear system modeling is presented. Based on the evolution computing and the optimal searching strategy, the computing model is able to find candidate regions and further to search global solutions in the regions. HSA is one of the adaptive optimal search algorithms and the heuristic information is used to form the next optinal searching directions and steps. It has the characters of less computing time and fast searching speed. This is very helpful in nonlinear system dynamic modeling. It is generally applied in real-time identification of many dynamic control process systems. Several nonlinear functions are used to test in MoHSA. The results show the ability of MoHSA model and the features of simplicity and flexibility. Finally, a real-time control system is discussed.