Design of optimal length low-dispersion FBG filter using covariance matrix adapted evolution

Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore
IEEE Photonics Technology Letters (Impact Factor: 2.11). 11/2005; 17(10):2119 - 2121. DOI: 10.1109/LPT.2005.854350
Source: IEEE Xplore


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.

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    • "Owing to the learning process, the CMAES algorithm performs the search independent of the coordinate system , reliably adapts topologies of arbitrary functions, and significantly improves convergence rate especially on non-separable and/or badly scaled objective functions. CMAES algorithm has been successfully applied in varieties of engineering optimization problems [16]. This algorithm outperforms all other similar classes of learning algorithms on the benchmark multimodal functions [17]. "
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    ABSTRACT: This paper is aimed at exploring the performance of the various evolutionary algorithms on multi-area economic dispatch (MAED) problems. The evolutionary algorithms such as the Real-coded Genetic Algorithm (RGA), Particle Swarm Optimization (PSO), Differential Evolution (DE) and Covariance Matrix Adapted Evolution Strategy (CMAES) are considered. To determine the efficiency and effectiveness of various EAs, they are applied to three test systems; including 4, 10 and 120 unit power systems are considered. The optimal results obtained using various EAs are compared with Nelder–Mead simplex (NMS) method and other relevant methods reported in the literature. To compare the performances of various EAs, statistical measures like best, mean, worst, standard deviation and mean computation time over 20 independent runs are taken. The simulation experiments reveal that CMAES algorithm performs better in terms of solution quality and consistency. Karush–Kuhn–Tucker (KKT) conditions are applied to the solutions obtained using EAs to verify optimality. It is found that the obtained results are satisfying the KKT conditions and confirm the optimality. Also, the effectiveness of KKT error based stopping criterion is demonstrated.
    Full-text · Article · Sep 2009 · International Journal of Electrical Power & Energy Systems
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    • "Para estes exemplos, muitas vezes são necessários projetos de grades com características sofisticadas de refletividade, transmissividade ou dispersão, que não podem ser realizados através de técnicas analíticas convencionais auxiliadas por computador. Assim, naturalmente as meta-heurísticas, como o algoritmo genético (AG), tornam-se importantes alternativas [4] [5]. Dentre todas as meta-heurísticas, o AGé certamente um algoritmo bem estabelecido e documentado. "

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