Abstract
Optimization techniques inspired by swarm intelligence (SI) have become increasingly popular during the last two decades. These techniques are based on the idea that groups of extremely simple agents with little or no organization can exhibit complex and intelligent behavior by using simple local rules and communication mechanisms. Thanks to this intelligent behavior, a group of social agents can carry out actions on a complex level and form decentralized and self-organizational systems. The advantage of these optimization approaches over traditional techniques is their robustness and flexibility, making SI especially appropriated to deal with complex optimization problems. In this chapter we introduce the concept of computational swarm intelligence; we present an overview of the most important optimization techniques inspired by swarm intelligence and examine the research contributions to the application of SI metaheuristics in different problems related to optimal management of aquaculture farms. As example of application we will present a particle swarm optimization (PSO) algorithm based on a bioeconomic model that helps managers of aquaculture enterprises in the process of decision-making, maximizing gross margin and minimizing operational risk.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Atia DM, Fahmy FH, Ahmed NM, Dorrah HT (2012) Optimal sizing of a solar water heating system based on a genetic algorithm for an aquaculture system. Math Comput Model 55(3–4):1436–1449
Beni G (1998) The concept of cellular robotic systems. In: Proceedings of the IEEE international symposium on intelligent systems. IEEE, Piscataway, pp 57–62
Bjørndal T, Lane DE, Weintraub A (2004) Operational research models and the management of fisheries and aquaculture: a review. Eur J Oper Res 156:533–540
Blum C, Li X (2008) Swarm intelligence in optimization. In: Blum C, Merkle D (eds) Swarm intelligence: introduction and applications. Springer, Berlin
Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv 35(3):268–308
Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems. Oxford University Press, New York
Chau KW (2005) Algal bloom prediction with particle swarm optimization algorithm. Lect Notes Comput Sci 3801:645–650
Chen DG, Hargreaves NB, Ware DM, Liu Y (2000) A fuzzy logic model with genetic algorithm for analyzing fish stock-recruitment relationships. Can J Fish Aquat Sci 57(9):1878–1887
Chen P, Wiley EO, Mcnyset KM (2007) Ecological niche modeling as a predictive tool: silver and bighead carps in North America. Biol Invasions 9(1):43–51
D’Angelo DJ, Howard LM, Meyer JL, Gregory SV, Ashkenas LR (1995) Ecological uses for genetic algorithms: predicting fish distributions in complex physical habitats. Can J Fish Aquat Sci 52(9):1893–1908
Deng C, Wei X, Guo L (2006) Application of neural network based on PSO algorithm in prediction model for dissolved oxygen in fishpond. In: Proceedings of the World Congress on Intelligent Control and Automation (WCICA), pp 9401–9405
Dorigo M (1992) Optimization, learning and natural algorithms. Ph.D. Thesis, Politecnico di Milano
Dorigo M, Stützle T (2004) Ant colony optimization. Bradford/MIT, Cambridge
Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp 39–43
Engelbrecht AP (2005) Fundamental of computational swarm intelligence. Wiley, Chichester
Glover F (1986) Future paths for integer programming and links to artificial intelligence. Comput Oper Res 13:533–549
Gutiérrez-Estrada JC, Pulido-Calvo I, De la Rosa I, Marchini B (2012) Modeling inflow rates for the water exchange management in semi-intensive aquaculture ponds. Aquac Eng 48:19–30
Hoffmeyer J (1994) The swarming body. In: Rauch I, Carr GF (eds) Semiotics around the world. In: Proceedings of the Fifth Congress of the International Association of Semiotic Studies, pp 937–940
Hormiga JA, Almansa E, Sykes AV, Torres NV (2010) Model based optimization of feeding regimens in aquaculture: application to the improvement of Octopus vulgaris viability in captivity. J Biotechnol 149(3):209–214
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks. Perth, Australia, pp 1942–1948
Kochenberger GA (2003) Handbook of metaheuristics. Springer, Berlin
Liao TW, Hu E, Tiersch TR (2012) Metaheuristic approaches to grouping problems in high-throughput cryopreservation operations for fish sperm. Appl Soft Comput 12(8):2040–2052
Liu S, Tai H, Ding Q, Li D, Xu L, Wei Y (2011) A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction. Math Comput Model. doi:10.1016/j.mcm.2011.11.021
Mardle SJ, Pascoe S, Tamiz M (2000) An investigation of genetic algorithms for the optimization of multi-objective fisheries bioeconomic models. Int Trans Oper Res 7:33–49
Osman IH, Laporte G (1996) Metaheuristics: a bibliography. Ann Oper Res 63:513–623
Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization: an overview. Swarm Intell 1(1):33–57
Stafford R (2008) A computational approach to ecological and economic sustainable harvest management strategies in a multi-species context, with implications for cod recovery plans. Ecol Informat 3(1):105–110
Therriault TW, Herborg LM (2008) Predicting the potential distribution of the vase tunicate Ciona intestinalis in Canadian waters: informing a risk assessment. ICES J Mar Sci 65(5):788–794
Wang EJ, Tsai DM, Su TS, Lin KY (2012) Simulated annealing for cost-effective transport of live aquaculture products. Aquac Econ Manag 6(1):68–95
Xuemei H, Yingzhan H, Xingzhi Y (2011) The soft measure model of dissolved oxygen based on RBF network in ponds. In: Proceedings 4th International Conference on Information and Computing, ICIC 2011, pp 38–41
Yang XS (2012) Nature-inspired metaheuristic algorithms: success and new challenges. J Comp Eng Inform Technol 1(1). doi: 10.4172/jceit.1000e101
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer Science + Business Media New York
About this chapter
Cite this chapter
Cobo, A., Llorente, I., Luna, L. (2015). Swarm Intelligence in Optimal Management of Aquaculture Farms. In: Plà -Aragonés, L. (eds) Handbook of Operations Research in Agriculture and the Agri-Food Industry. International Series in Operations Research & Management Science, vol 224. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2483-7_10
Download citation
DOI: https://doi.org/10.1007/978-1-4939-2483-7_10
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4939-2482-0
Online ISBN: 978-1-4939-2483-7
eBook Packages: Business and EconomicsBusiness and Management (R0)