An efficient dynamical evolutionary algorithm for global optimization

Abstract

In this paper, we introduce a new dynamical evolutionary algorithm (DEA) that aims to find the global optimum and give the theoretical explanation from statistical mechanics. The algorithm has been evaluated numerically using a wide set of test functions which are nonlinear, multimodal and multidimensional. The numerical results show that it is possible to obtain global optimum or more accurate solutions than other methods for the investigated hard problems.

Keywords:

• Dynamical evolutionary algorithm
• Statistical mechanics
• Global optimization
• Genetic algorithm