A hybrid evolutionary computation algorithm for global optimization

This study proposes a new hybrid algorithm for solving small to large-scale continuous global optimization problems. It comprises evolutionary computation algorithm featuring a novel adaptive elitism strategy and a sequential quadratic programming algorithm; combined in a collaborative portfolio with a validation procedure. The sequential quadratic programming is a gradient based local search method designed to derive effective search directions by using exact Hessians obtained via a vectorized forward accumulation of derivatives technique. The proposed hybrid design aim was to ensure that the two algorithms complement each other by effectively exploring and exploiting the problem search space. Experimental results justify that an adept hybridization of evolutionary algorithms with a suitable local search method could yield a robust and efficient means of solving wide range of global optimization problems.