A novel evolutionary algorithm based on an orthogonal design for dynamic optimization problems (ODEA)

This paper introduces an orthogonal evolutionary algorithm for dynamic optimization problems with continuous variables (called ODEA). Its population does not consist of individuals, but rather of "niches". Each niche selects the best solution found so far as its representative. An orthogonal design method is employed to find a potentially good solution that may become the representative of the niche in the mutation operator. We employ a complex benchmark to test the new approach. Numerical experiments show that the ODEA algorithm performs a lot better than the SOS (self organizing scouts) algorithm in Branke, Kaufler, Schmidt and Schmeck, (2000).