Name
Abstract | ||
|---|---|---|
Interior lighting design is a challenging task where are involved multiple constraints that need to be optimized for producing an accurate illumination avoiding possible glare.This paper, then, takes up the issue of providing a computational tool able to produce a proper lighting plan in interior spaces for a comfortable and optimal vision in all environments, taking also into account the energy consumption as little as possible.For finding acceptable solutions we have used the metaphor of the genetic evolution in a multi-objective model, where individuals are lists of possible light sources, their positions and lighting levels.For finding acceptable solutions we have used the metaphor of the genetic evolution in a multi-objective model, where every individual is a list of light sources; their positions; and lighting levels. Further, for properly evaluating each individual, we have developed two conflicting objective functions, one for optimizing the level of brightness, and the second one for maximising the energy saving, satisfying, obviously, the additional constraints to respect the architectural structure to be lighted. From the randomly initial population of individuals generations are constructed using crossover and mutation operators, whilst the fittest offspring is preserved via an elitist Pareto-dominance selection approach. In addition to the multi-objective genetic algorithm, the 3D graphic software Blender has been used in order to reproduce the architectural space to be lighted, with the aim to evaluate then, the accuracy and uniformity of the produced lighting through a physical simulation of its brightness. The main goal of the developed tool is to provide to the designer (i.e. the decision maker) a set of interiors illumination design options, for the given environment to be lit, ensuring (i) uniform illumination distribution; (ii) accuracy of the illumination produced; (iii) avoiding harsh brightness, and glare; and (iv) low energy consumptions.Two case studies have been considered in our evaluation experiments, and for each of these the algorithm was performed on two different instances and with different types of complexity respectively. |
Year | Venue | Field |
|---|---|---|
2017 | IJCCI | Population,Mathematical optimization,Crossover,Computer science,Multi-objective optimization,Survival of the fittest,Graphics software,Energy consumption,Brightness,Genetic algorithm |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alice Plebe | 1 | 0 | 0.34 |
| vincenzo cutello | 2 | 553 | 57.63 |
| Mario Pavone | 3 | 212 | 19.41 |