Evolutionary optimization algorithms for nonimaging optical design

Abstract

Evolutionary optimization algorithms have been recently introduced as nonimaging optics design techniques. Unlike optimization of imaging systems, non sequential ray tracing simulations and complex non centred systems design must be considered, adding complexity to the problem. The Merit Function (MF) is a key element in the automatic optimization algorithm, nevertheless the selection of each objective's weight, {wi}, inside merit function needs a previous trial and error process for each optimization. The problem then is to determine appropriate weights value for each objective. In this paper we propose a new Dynamic Merit Function, DMF, with variable weight factors {wi(n)}. The proposed algorithm, automatically adapts weight factors, during the evolution of the optimization process. This dynamic merit function avoids the previous trial and error procedure selecting the right merit function and provides better results than conventional merit functions (CMF). Also we analyse the Multistart optimization algorithm applied in the flowline nonimaging design technique.