In this paper we present a new evolutionary method for complex-process optimization. It is partially based on the principles of the scatter search methodology, but it makes use of innovative strategies to be more effective in the context of complex-process optimization using a small number of tuning parameters. In particular, we introduce a new combination method based on pathrelinking, which considers a broader area around the population members than previous combination methods. We also use a population- update method which improves the balance between intensification and diversification. New strategies to intensify the search and to escape from suboptimal solutions areal so presented. The application of the proposed evolutionary algorithm to different sets of bothstate-of-the-art continuous global optimization and complex-process optimization problems reveals that it is robust and efficient for the type of problems intended to solve, outperforming the results obtained with other methods found in the literature.

The team at CSIC acknowledges the financial support from Spanish MICINN Project MultiSysBioDPI2008-06880-C03-02.Researchby Rafael Martí has been partially supported by the Ministerio deCiencia e Innovación of Spain(GrantRef.TIN2006-02696).

10 páginas, 7 figuras, 7 tablas

Peer reviewed