@Article{AlbuquerqueSousMont:2016:MuApAu,
               author = "Albuquerque, Br{\'a}ulio Fonseca Carneiro de and Sousa, Fabiano 
                         Luis de and Montes, Amauri Silva",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Multi-objective approach for the automatic design of optical 
                         systems",
              journal = "Optics Express",
                 year = "2016",
               volume = "24",
               number = "6",
                pages = "6619--6643",
                month = "Mar.",
             keywords = "lgorithms, Design, Economic and social effects, Efficiency, Least 
                         squares approximations, Lenses, Multiobjective optimization, 
                         Optical glass, Optical instrument lenses, Optical systems, 
                         Optimization, Orbits.",
             abstract = "An innovative method for the automatic design of optical systems 
                         is presented and verified. The proposed method is based on a 
                         multiobjective evolutionary memetic optimization algorithm. The 
                         multi-objective approach simultaneously, but separately, addresses 
                         the image quality, tolerance, and complexity of the system. The 
                         memetic technique breaks down the search for optical designs in to 
                         three different parts or phases: optical glass selection, 
                         exploration, and exploitation. The optical glass selection phase 
                         defines the most appropriate set of glasses for the system under 
                         design. The glass selection phase limits the available glasses 
                         from hundreds to just a few, drastically reducing the design space 
                         and significantly increasing the efficiency of the automatic 
                         design method. The exploration phase is based on an evolutionary 
                         algorithm (EA), more specifically, on a problem-tailored 
                         generalized extremal optimization (GEO) algorithm, named Optical 
                         GEO (O-GEO). The new EA incorporates many features customized for 
                         lens design, such as optical system codification and diversity 
                         operators. The trade-off systems found in the exploration phase 
                         are refined by a local search, based on the damped least square 
                         method in the exploitation phase. As a result, the method returns 
                         a set of trade-off solutions, generating a Pareto front. Our 
                         method delivers alternative and useful insights for the compromise 
                         solutions in a lens design problem. The efficiency of the proposed 
                         method is verified through realworld examples, showing excellent 
                         results for the tested problems.",
                  doi = "10.1364/OE.24.006619",
                  url = "http://dx.doi.org/10.1364/OE.24.006619",
                 issn = "1094-4087",
             language = "en",
        urlaccessdate = "27 abr. 2024"
}