Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
GCIS '10 Proceedings of the 2010 Second WRI Global Congress on Intelligent Systems - Volume 01
Expert Systems with Applications: An International Journal
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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This paper presents a new method for calculating the design of roadway lighting. Apart from its accuracy, this method, which is based on a multi-objective evolutionary algorithm, has the added advantage of enhancing the energy efficiency of lighting installations. This is positive because the economic use of energy resources is evidently a priority in the world today. In our study, an exhaustive calibration process was used to fine-tune the accuracy and precision of the new method presented. The results obtained were then compared with those of DIALUX, a well-known free software program that is frequently used for the design of lighting installations. In the second phase of this research, the lighting installation was made more complex in order to verify the applicability of this new method to a wide range of different contexts.