A Class of Filled Functions with Parameters Adjusted by Various Strategies and Its Applications to Global Optimization

A class of filled functions whose parameters can be adjusted by various methods is firstly proposed and analyzed theoretically. Secondly, different strategies are suggested to use the filled functions and measures are given to construct the more effective filled function. Finally, several examples with numerical experiment results are given, which illustrates that the proposed scheme is effective, and moreover it improves the computational ability of existing filled functions.