Design of optimal length low-dispersion FBG filter using covariance matrix adapted evolution
ABSTRACT The design of a low-dispersion fiber Bragg grating (FBG) with an optimal grating length using covariance matrix adapted evolution strategy (CMAES) is presented. A novel objective function formulation is proposed for the optimal grating length low-dispersion FBG design. The CMAES algorithm employs adaptive learning procedure to identify correlations among the design parameters. The design of a low-dispersion FBG filter with 25-GHz (or 0.2 nm in the 1550-nm band) bandwidth is considered. Simulation results, obtained using the codes available in public domain (the codes are available from the third author), show that the CMAES algorithm is more appropriate for the practical design of length optimized FBG-based filters when compared with the other optimization methods.
- SourceAvailable from: scielo.brJournal of Microwaves, Optoelectronics and Electromagnetic Applications. 06/2011; 10(1):165-178.
- [Show abstract] [Hide abstract]
ABSTRACT: This article presents a covariance matrix adapted evolution strategy (CMAES) algorithm to solve dynamic economic dispatch (DED) problems. The DED is an extension of the conventional economic dispatch problem, in which optimal settings of generator units are determined with a predicted load demand over a period of time. In this article, the applicability and validity of the CMAES algorithm is demonstrated on three DED test systems with a sequential decomposition approach. The results obtained using the CMAES algorithm are compared with results obtained using the real-coded genetic algorithm, the Nelder–Mead simplex method, and other methods from the literature. To compare the performance of the various algorithms, statistical measures like best, mean, worst, standard deviation, and mean computation time over 20 independent runs are taken. The effect of population size on the performance of the CMAES algorithm is also investigated. The simulation experiments reveal that the CMAES algorithm performs better in terms of fuel cost and solution consistency. Karush–Kuhn–Tucker conditions are applied to the solutions obtained using the CMAES algorithm to verify optimality. It is found that the obtained results satisfy the Karush–Kuhn–Tucker conditions and confirm optimality.Engineering Optimization 07/2009; 41(7):635-657. · 1.23 Impact Factor