Exterior lighting computer automated design based on multi criteria parallel evolutionary algorithm: optimized designs for illumination quality and energy efficiency
Multi objective evolutionary algorithm to computer automated exterior lighting design.Web client integrated to a cluster of computers to provide lighting design service.Solution to optimize both illumination quality and energy efficiency.Case study solution presents 37.5% power consumption and +227.3% global uniformity. A proper professional lighting design implies in a continuous search for the best compromise between both low power consumption and better lighting quality. This search converts this design into a hard to solve multi objective optimization problem. Evolutionary algorithms are widely used to attack that type of hard optimization problems. However, professionals could not benefit from that kind of assistance since evolutionary algorithms have been unexplored by several commercial lighting design computer aided softwares. This work proposes a system based on evolutionary algorithms which implement a computer automated exterior lighting design both adequate to irregular shaped areas and able to respect lighting pole positioning constraints. The desired lighting design is constructed using a cluster of computers supported by a web client, turning this application into an efficient and easy tool to reduce project cycles, increase quality of results and decrease calculation times. This ELCAutoD EA system consists in a proposal for a parallel multi objective evolutionary algorithm to be executed in a cluster of computers with a Java remote client. User must choose lighting pole heights, allowed lamps and fixtures, as well as the simplified blue print of the area to be illuminated, marking the sub areas with restrictions to pole positioning. The desired average illuminance must also be informed as well as the accepted tolerance. Based on user informed data, the developed application uses a dynamic representation of variable size as a chromosome and the cluster executes the evolutionary algorithm using the Island model paradigm. Achieved solutions comply with the illumination standards requirements and have a strong commitment to lighting quality and power consumption. In the present case study, the evolved design used 37.5% less power than the reference lighting design provided by a professional and at the same time ensured a 227.3% better global lighting uniformity. A better lighting quality is achieved because the proposed system solves multi objective optimization problems by avoiding power wastes which are often unclear to a professional lighting engineer in charge of a given project.
Expert Syst. Appl.