Otimização de algoritmo evolucionário multiobjetivo paralelo para a geração automática de projetos de iluminação de áreas externas

• Autor(a) principal: Rocha, Hugo Xavier
• Data de Publicação: 2015
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Repositório Institucional da UFU
• Texto Completo: https://repositorio.ufu.br/handle/123456789/14360; https://doi.org/10.14393/ufu.te.2015.145
• topic: Iluminação; Programação paralela; Algoritmos evolucionários multiobjetivo paralelos; NSGA-II; Cromossomo variável; Iluminação elétrica; Programação paralela (Computação); Algoritmos paralelos; Lighting; Parallel programming; Evolutionary algorithms multiobjective parallel; Chromosome variable; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA

This paper presents the study of Parallel Multiobjective Evolutionary Algorithms to enable the automation of exterior lighting designs by computers and results in an optimized version of the algorithm. The resulting algorithm basically works with variable length chromosomes and for which intrinsic operators of crossover and mutation were created. The fitness function was determined through a statistical evaluation method (difference of means), thus enabling the comparison of how different options of fitness functions could impact the performance of the proposed parallel multi-objective evolutionary algorithm. The chosen fitness function enables to develop more efficiently automated designs for exterior lighting. Moreover, adding to the proposed evolutionary algorithm, an application was developed in which the user chooses which the heights of the poles, lamps and fixtures to use and also the layout of the area to be illuminated (allowed to be irregular). Within this area, can be defined sub-areas where there are restrictions on the placement of lighting poles. The user must be set average illumination with a respective tolerance range, though. As a case study, the area of an airport parking lot in the city of Uberlândia-MG (Brazil) is presented. Evolved designs present a low coefficient of variation evaluated for 30 runs. This demonstrates that the system is converging on designs for similar metrics. By identifying the worst and the best of designs achieved by the system for those executions, one could note that there are savings regarding installed capacity when compared to the design of reference: 37.5 % for the worst evolved design and 50.0 % for the best evolved design. Also, evolved designs have better lighting uniformity and energy efficiency, as well as their respective quantities of lighting poles have decreased.