A multi-objective optimization platform for artificial lighting system in commercial greenhouses

Abstract

Limited natural daylight in Nordic Countries means artificial lighting is a critical factor in industrial plant production. The electricity cost of artificial lights accounts for a large percentage of the overall cost of plant production. The optimal use of artificial lighting in plant production can be formulated as a multi-objective problem (MOP) to achieve optimal plant growth while minimizing electricity cost. In previous work, for solving this MOP, a Genetic Algorithm (GA) was used to create a Pareto Frontier (PF), which contains solutions representing a trade-off for using artificial lighting against plant production objectives. The PF was updated immediately once a non-dominated child-solution was found by comparing the dominance with solutions in the PF. Besides, in addition to the PF, the initial random population is also reused as a parent source in the evolution process. When the genetic evolution process terminated, a priority-based selection mechanism was used to select a final solution from the PF. In this paper, an alternative evolution strategy is proposed and compared with the previous GA evolution strategy. By this alternative strategy, all child-solutions are only compared with their parents during the evolution process, and the non-dominated child-solutions are collected into a candidate list. The PF is then updated at the end of each generation by comparing solutions on the PF with the collected candidate solutions. In this alternative strategy, the PF is the only source of parent-solution during the evolution process. In addition, a posterior normalization is implemented in the dominance evaluation, and social welfare metrics (SWs) are applied as an alternative to the priority-based selection mechanism to avoid the explicit ranking of objectives. The experimental results show that the proposed alternative evolution strategy outperforms the previous strategy on dramatically avoiding local minima.

Optimal Selection of Distribution, Power and Type of Luminaires for Street Lighting Designs Using Multi-criteria Decision

“…Since artificial light sources that consume electrical energy are used, they can account for 21% to 40% of energy consumption in buildings in developing countries and up to 15% in industrialized countries [14,17]. This mode of operation also affects the cost of operation and maintenance, which is covered by public administration and can amount to a significant value [8,20].…”

“…Currently, research has been conducted where multi-criteria models are applied to the design of street lighting systems, tunnels, and indoor spaces, as developed by [6,13,14,20,24], where multicriteria models are implemented for the problem, while the use of genetic algorithms is explored in the works of [11,15,16,21] y [26]. These studies differ in their optimization objectives and the number of criteria considered.…”

Optimal Selection of Distribution, Power, and Type of Luminaires for Street Lighting Designs Using Multi-Criteria Decision Model

“…Since artificial light sources that consume electrical energy are used, they can account for 21% to 40% of energy consumption in buildings in developing countries and up to 15% in industrialized countries [9,10]. This mode of operation also affects the cost of operation and maintenance, which is covered by public administration and can amount to a significant value [11,12].…”

“…Currently, research has been conducted where multi-criteria models are applied to the design of street lighting systems, tunnels, and indoor spaces, as developed by [9,12,[17][18][19]. include multi-criteria models implemented for the problem, while the use of genetic algorithms is explored in the works of [5,15,[20][21][22].…”

“…All lighting projects comply with constraints related to the level and uniformity of illumination; some consider electrical power in the cost calculation, while others incorporate user feedback. [9,12,13,15,[17][18][19][20][21][22].…”