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 prod...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2021-09-01
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| Series: | Energy Informatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42162-021-00162-8 |
| _version_ | 1818442304318341120 |
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| author | Ying Qu Anders Clausen Bo Nørregaard Jørgensen |
| author_facet | Ying Qu Anders Clausen Bo Nørregaard Jørgensen |
| author_sort | Ying Qu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T18:42:01Z |
| format | Article |
| id | doaj.art-47088edb9ddd4d0f98458064335252e3 |
| institution | Directory Open Access Journal |
| issn | 2520-8942 |
| language | English |
| last_indexed | 2024-12-14T18:42:01Z |
| publishDate | 2021-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj.art-47088edb9ddd4d0f98458064335252e32022-12-21T22:51:27ZengSpringerOpenEnergy Informatics2520-89422021-09-014S212010.1186/s42162-021-00162-8A multi-objective optimization platform for artificial lighting system in commercial greenhousesYing Qu0Anders Clausen1Bo Nørregaard Jørgensen2The Maersk Mc-Kinney Moller Institute, University of Southern DenmarkThe Maersk Mc-Kinney Moller Institute, University of Southern DenmarkThe Maersk Mc-Kinney Moller Institute, University of Southern DenmarkAbstract 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.https://doi.org/10.1186/s42162-021-00162-8Multi-objective optimization problem (MOP)Social Welfare metrics (SWs)Commercial greenhouseMulti-objective evolutionary algorithm (MOEA)Genetic Algorithm (GA) |
| spellingShingle | Ying Qu Anders Clausen Bo Nørregaard Jørgensen A multi-objective optimization platform for artificial lighting system in commercial greenhouses Energy Informatics Multi-objective optimization problem (MOP) Social Welfare metrics (SWs) Commercial greenhouse Multi-objective evolutionary algorithm (MOEA) Genetic Algorithm (GA) |
| title | A multi-objective optimization platform for artificial lighting system in commercial greenhouses |
| title_full | A multi-objective optimization platform for artificial lighting system in commercial greenhouses |
| title_fullStr | A multi-objective optimization platform for artificial lighting system in commercial greenhouses |
| title_full_unstemmed | A multi-objective optimization platform for artificial lighting system in commercial greenhouses |
| title_short | A multi-objective optimization platform for artificial lighting system in commercial greenhouses |
| title_sort | multi objective optimization platform for artificial lighting system in commercial greenhouses |
| topic | Multi-objective optimization problem (MOP) Social Welfare metrics (SWs) Commercial greenhouse Multi-objective evolutionary algorithm (MOEA) Genetic Algorithm (GA) |
| url | https://doi.org/10.1186/s42162-021-00162-8 |
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