A Heuristic Evolutionary Multi-Objective Method for Indoor LED Lighting Optimization with DIALux and Experimental Validation
DOI:
https://doi.org/10.59247/jfsc.v4i2.425Keywords:
Indoor LED Lighting, Multi-Objective Evolutionary Algorithm, Constraint-Aware Heuristic Method, DIALux, Experimental ValidationAbstract
Indoor lighting optimization is commonly addressed using population-based metaheuristic algorithms such as the genetic algorithm and multi-objective particle swarm optimization. However, these methods generally rely on crossover, mutation, or swarm-update operators, resulting in relatively high computational complexity while placing greater emphasis on algorithmic optimization than on practical implementation and experimental validation. This study proposes a heuristic evolutionary multi-objective method for indoor LED lighting optimization that integrates constraint-aware candidate generation, lexicographical multi-objective ranking, and photometric evaluation to determine energy-efficient lighting layouts. Unlike conventional evolutionary algorithms, the proposed method directly generates feasible candidate solutions without employing crossover or mutation operators, thereby reducing computational complexity while maintaining solution quality for structured indoor lighting layouts. The optimized lighting configurations were validated through numerical photometric calculations based on SNI 6197:2020, DIALux simulations using a Lambertian photometric model, and direct luxmeter measurements. Experimental validation confirmed that the optimized lighting layouts satisfied the required illuminance and uniformity criteria, with average illuminance deviations ranging from 6% to 17% between computational predictions and practical measurements. The optimized lighting layouts also achieved Lighting Power Density (LPD) values ranging from 4.89 to 5.50 W/m², which are below the maximum allowable limit specified by SNI 6197:2020, demonstrating that the proposed optimization framework effectively reduces energy consumption while maintaining the required lighting performance. The proposed method provides a practical alternative to conventional evolutionary optimization methods by integrating efficient multi-objective optimization with comprehensive simulation and experimental validation, enabling reliable, energy-efficient, and standards-compliant indoor LED lighting design.
References
S. Bhattacharya, S. Bhattacharya, A. Das, S. Mahata, and S. Biswas, “A novel framework for the assessment of indoor lighting solutions and its application for model learning spaces of a higher educational institution considering energy efficiency and human factors,” Energy and Buildings, vol. 336, p. 115625, 2025, https://doi.org/10.1016/j.enbuild.2025.115625.
K. R. Wagiman, M. N. Abdullah, M. Y. Hassan, and N. H. Mohammad Radzi, “A new metric for optimal visual comfort and energy efficiency of building lighting system considering daylight using multi-objective particle swarm optimization,” Journal of Building Engineering, vol. 43, p. 102525, 2021, https://doi.org/10.1016/j.jobe.2021.102525.
M. Zhu, X. Zhang, D. Chen, and Y. Gong, “Impact of lighting environment on human performance and prediction modeling of personal visual comfort in enclosed cabins,” Science of the Total Environment, vol. 927, p. 171970, 2024, https://doi.org/10.1016/j.scitotenv.2024.171970.
S. Roy, P. Satvaya, and S. Bhattacharya, “Effects of indoor lighting conditions on subjective preferences of task lighting and room aesthetics in an Indian tertiary educational institution,” Building and Environment, vol. 249, p. 111119, 2024, https://doi.org/10.1016/j.buildenv.2023.111119.
H. Atma, F. Ruzzenenti, and M. A. van den Broek, “Exploring the evolution of long-term electricity demand and load curves in emerging economies: A case study of Indonesia’s energy transition,” Energy Strategy Reviews, vol. 60, p. 101805, 2025, https://doi.org/10.1016/j.esr.2025.101805.
X. Wu, H. Zhang, T. S. T. Ng, and A. C. K. Lai, “Improving far-UVC disinfection efficiency in portable devices: Optical enhancement and agent-based lamps’ layout optimization,” Building and Environment, vol. 290, p. 114138, 2026, https://doi.org/10.1016/j.buildenv.2025.114138.
H. Rocha, I. S. Peretta, G. F. M. Lima, L. G. Marques, and K. Yamanaka, “Exterior lighting computer-automated design based on multi-criteria parallel evolutionary algorithm: Optimized designs for illumination quality and energy efficiency,” Expert Systems with Applications, vol. 45, pp. 208–222, 2016, https://doi.org/10.1016/j.eswa.2015.09.046.
J. Q. Qu, Q. L. Xu, and K. X. Sun, “Optimization of Indoor Luminaire Layout for General Lighting Scheme Using Improved Particle Swarm Optimization,” Energies, vol. 15, no. 4, p. 1482, 2022, https://doi.org/10.3390/en15041482.
E. Guerry, C. D. Gǎlǎtanu, L. Canale, and G. Zissis, “Optimizing the luminous environment using DiaLUX software at ‘constantin and Elena’ Elderly House-Study Case,” Procedia Manufacturing, vol. 32, pp. 466–473, 2019, https://doi.org/10.1016/j.promfg.2019.02.241.
Z. Zhao, Z. Gao, H. Wang, F. Meng, X. Zhu, and F. Xu, “DIALux evo and Ladybug + Honeybee: A review of performance and application in electric lighting environment,” Building and Environment, vol. 285, p. 113570, 2025, https://doi.org/10.1016/j.buildenv.2025.113570.
L. Xu, H. Zhu, Y. Shen, T. Liu, L. Zhou, and S. Feng, “Lighting efficiency optimization method based on artificial neural networks and metaheuristic algorithms,” Energy and Buildings, vol. 347, p. 116251, 2025, https://doi.org/10.1016/j.enbuild.2025.116251.
B. Vojdani, M. Rahbar, M. Fazeli, M. Hakimazari, and H. W. Samuelson, “Comparative study of optimization methods for building energy consumption and daylighting performance,” Energy and Buildings, vol. 323, p. 114753, 2024, https://doi.org/10.1016/j.enbuild.2024.114753.
M. S. Javed, T. Ma, J. Jurasz, S. Ahmed, and J. Mikulik, “Performance comparison of heuristic algorithms for optimization of hybrid off-grid renewable energy systems,” Energy, vol. 210, p. 118599, 2020, https://doi.org/10.1016/j.energy.2020.118599.
A. M. Adrian, A. Utamima, and K. J. Wang, “A comparative study of GA, PSO and ACO for solving construction site layout optimization,” KSCE Journal of Civil Engineering, vol. 19, no. 3, pp. 520–527, 2015, https://doi.org/10.1007/s12205-013-1467-6.
M. Sedghi, A. Ahmadian, and M. Aliakbar-Golkar, “Assessment of optimization algorithms capability in distribution network planning: Review, comparison and modification techniques,” Renewable and Sustainable Energy Reviews, vol. 66, pp. 415–434, 2016, https://doi.org/10.1016/j.rser.2016.08.027.
F. Cassol, P. S. Schneider, F. H. R. França, and A. J. Silva Neto, “Multi-objective optimization as a new approach to illumination design of interior spaces,” Building and Environment, vol. 46, no. 2, pp. 331–338, 2011, https://doi.org/10.1016/j.buildenv.2010.07.028.
X. Zhang, J. Wang, Y. Zhou, H. Wang, N. Xie, and D. Chen, “A multi-objective optimization method for enclosed-space lighting design based on MOPSO,” Building and Environment, vol. 250, p. 111185, 2024, https://doi.org/10.1016/j.buildenv.2024.111185.
J. L. J. Pereira, M. B. Francisco, C. A. Diniz, G. Antônio Oliver, S. S. Cunha, and G. F. Gomes, “Lichtenberg algorithm: A novel hybrid physics-based meta-heuristic for global optimization,” Expert Systems with Applications, vol. 170, p. 114522, 2021, https://doi.org/10.1016/j.eswa.2020.114522.
S. Carlucci, F. Causone, F. De Rosa, and L. Pagliano, “A review of indices for assessing visual comfort with a view to their use in optimization processes to support building integrated design,” Renewable and Sustainable Energy Reviews, vol. 47, pp. 1016–1033, 2015, https://doi.org/10.1016/j.rser.2015.03.062.
S. Shojaee Barjoee and S. Gendler, “Sustainable illumination: Experimental and simulation analysis of illumination for workers wellbeing in the workplace,” Heliyon, vol. 10, no. 24, p. e40745, 2024, https://doi.org/10.1016/j.heliyon.2024.e40745.
S. Jang, Y. K. Baik, and S. Kim, “Analyzing the effects of illuminance variations on occupants’ visual perceptions to determine permissible dimming controls of lighting in a small office,” Building and Environment, vol. 254, p. 111322, 2024, https://doi.org/10.1016/j.buildenv.2024.111322.
T. Yee, J. J. Yang, J. J. James, S. Ma, and J. D. Marsh, “Quantifying light penetration in short tunnels: Development and validation of a numerical model,” Journal of Traffic and Transportation Engineering (English Edition), 2026, https://doi.org/10.1016/j.jtte.2025.04.003.
M. Hakimazari et al., “Multi-objective optimization of daylight illuminance indicators and energy usage intensity for office space in Tehran by genetic algorithm,” Energy Reports, vol. 11, pp. 3283–3306, 2024, https://doi.org/10.1016/j.egyr.2024.03.011.
K. Nath, “Evolutionary optimization techniques for scalable and adaptive problem solving,” Data Science and Informetrics, vol. 6, no. 1, p. 100031, Apr. 2026, https://doi.org/10.1016/j.dsim.2026.01.001.
D. Sarriá et al., “Light and current generation system for measuring the behaviour of the Norway lobster,” Measurement: Journal of the International Measurement Confederation, vol. 69, pp. 180–188, 2015, https://doi.org/10.1016/j.measurement.2015.03.002.
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