Agricultural system modelling with Ant Colony Optimization

• Autorzy: Fidanova Stefka , Dimov Ivan , Angelova Denitsa , Ganzha Maria

Identyfikatory

DOI

10.15439/2022F10

• Konferencja: Federated Conference on Computer Science and Information Systems (17 ; 04-07.09.2022 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

Cereals contribute significantly to humanity's livelihood. They are a source of more food energy worldwide than any other group of crops. Their production contributes considerably to the total global anthropogenic greenhouse gas (GHGs) emissions. In this study we propose a basic bio-economic farm model (BEFM) solved with the help of Ant Colony Optimization (ACO) methodology. We aim to assess farm profits and risks considering various types of policy incentives and adverse weather events. The proposed model can be applied to any annual crop.

Słowa kluczowe

EN

agricultural system modeling; bio-economic farm model; Ant Colony Optimization; ACO; metaheuristics

PL

rolnictwo; gospodarstwo rolne; optymalizacja kolonii mrówek; ACO; metaheurystyka

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 30
• Strony: 329--332
• Opis fizyczny: Bibliogr. 14 poz., tab., wz.

Twórcy

autor

Fidanova Stefka

• Institute of Information and Communication Technology Bulgarian Academy of Sciences Sofia, Bulgaria

autor

Dimov Ivan

• Institute of Information and Communication Technology Bulgarian Academy of Sciences Sofia, Bulgaria

autor

Angelova Denitsa

• Euro-Mediterranean Center on Climate Change Ca’ Foscari University of Venice Venice, Italy

autor

Ganzha Maria

• System Research Institute Polish Academy of Sciences Warsaw and Management Academy Warsaw, Poland

Bibliografia

• 1. Reidsma P., Janssen S., Jansen J., van Ittersum M. K., On the development and use of farm models for policy impact assessment in the European Union – A review, Agricultural Systems Vol. 159, 2018, 111-125.
• 2. Djanibekov U., Finger R., Agricultural risks and farm land consolidation process in transition countries: The case of cotton production in Uzbekistan, Agricultural Systems Vol. 164, 2018, 223-235.
• 3. Spiegel A., Severini S., Britz W., Coletta A., Step-by-step development of a model simulating returns on farm from investments: the example of hazelnut plantation in Italy: The example of hazelnut plantation in Italy, Bio-based and Applied Economics Vol. 9, 2020, 53-83.
• 4. Spiegel A., Britz W., Djanibekov U., Finger R., Stochastic-dynamic modelling of farm-level investments under uncertainty, Environmental Modelling and Sortware Vol. 127, 2020, 1-14.
• 5. Rössert S., Gosling E., Gandorfer M., Knoke T., Woodchips or potato chips? How enhancing soil carbon and reducing chemical inputs influence the allocation of cropland, Agricultural Systems Vol. 198, 2022, 1-16.
• 6. Britz W., Ciaian P., Gocht A., Kanellopoulos A., Kremmydas D., Müller M., Petsakos A., Reidsma P., A design for a generic and modular bio-economic farm model, Agricultural Systems Vol. 191, 2021, 1-14.
• 7. Dorigo M, Stutzle T., Ant Colony Optimization, MIT Press, 2004.
• 8. Bonabeau E., Dorigo M. and Theraulaz G., Swarm Intelligence: From Natural to Artificial Systems, New York,Oxford University Press, 1999.
• 9. Fidanova S., Roeva O., Paprzycki M., Gepner P., InterCriteria Analysis of ACO Start Startegies, Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, 2016, 547-550.
• 10. Fidanova S., Luquq G., Roeva O., Paprzycki M., Gepner P., Ant Colony Optimization Algorithm for Workforce Planning, FedCSIS’2017, IEEE Xplorer, IEEE catalog number CFP1585N-ART, 2017, 415-419.
• 11. Roeva O., Fidanova S., Luque G., Paprzycki M., Gepner P., Hybrid Ant Colony Optimization Algorithm for Workforce Planning, FedCSIS’2018, IEEE Xplorer, 2018, 233-236.
• 12. Mucherino A., Fidanova S., Ganzha M., Introducing the environment in ant colony optimization, Recent Advances in Computational Optimization, Studies in Computational Intelligence, Vol. 655, 2016, 147–158.
• 13. IPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change ,Masson-Delmotte, V., P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu and B. Zhou (eds.). Cambridge University Press, 2021.
• 14. European Union policies, https://ec.europa.eu/info/strategy/priorities-2019-2024/european-green-deal_en?msclkid=86feef9bbafb11ec8c045931e097187b

Uwagi

1. The presented work is partially supported by the grant No BG05M2OP011-1.001-0003, financed by the Science and Education for Smart Growth Operational Program and co-financed by European Union through the European structural and Investment funds. The work is supported too by National Scientific Fund of Bulgaria under the grant DFNI KP-06-N52/5 and bilateral project IC-PL/01/2022-2021.

2. Short article

3. Track 6: 15th International Workshop on Computational Optimization

4. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).