Abstract
The development of a low cost and intelligent swarm of autonomous underwater vehicles (AUVs) is the long term goal of this research. Such a swarm of AUVs could be used, for example, for locating submarine sources of interest, such as dumped radioactive waste or ammunition. This is a difficult problem for direct search algorithms, because in large areas of the search space, gradient information is not available. The overall search strategy used in the work is based on particle swarm optimisation (PSO). The number of AUVs here is small due to costs and availability. The performance of PSO depends on the right choice of control parameters. Therefore this paper presents an empirical study of the effects of different search parameter settings on the performance of PSO, used with a swarm of three AUVs in a dynamic environment. A simulation of submarine groundwater discharge, based on Cellular Automata, is used as a dynamic test environment. It was shown in this research that PSO in this configuration is robust against control parameter settings.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Zielinski, O., Busch, J.A., Cembella, A.D., Daly, K.L., Engelbrektsson, J., Hannides, A.K., Schmidt, H.: Detecting marine hazardous substances and organisms: sensors for pollutants, toxins and pathogens. Ocean Sci. 5, 329–349 (2009)
Zielinski, O.: Airborne pollution surveillance using multi-sensor systems – new sensors and algorithms for improved oil spill detection and polluter identification. Sea Technol. 44(10), 28–32 (2003)
Moore, W.S.: The effect of submarine groundwater discharge on the ocean. Annu. Rev. Mar. Sci. 2, 59–88 (2010)
Nelson, C.E., Donahue, M.J., Dulaiova, H., Goldberg, S.J., La Valle, F.F., Lubarsky, K., Miyano, J., Richardson, C., Silbiger, N.J., Thomas, F.I.: Fluorescent dissolved organic matter as a multivariate biogeochemical tracer of submarine groundwater discharge in coral reef ecosystems. Mar. Chem. 177, 232–243 (2015)
Beck, M., Reckhardt, A., Amelsberg, J., Bartholomä, A., Brumsack, H.J., Cypionka, H., Dittmar, T., Engelen, B., Greskowiak, J., Hillebrand, H., Holtappels, M., Neuholz, R., Köster, J., Kuypers, M.M.M., Massmann, G., Meier, D., Niggemann, J., Paffrath, R., Pahnke, K., Rovo, S., Striebel, M., Vandieken, V., Wehrmann, A., Zielinski, O.: The drivers of biogeochemistry in beach ecosystems: a crossshore transect from the dunes to the low water line. Mar. Chem. 190, 35–50 (2017)
Evans, T.B., Wilson, A.M.: Groundwater transport and the freshwater–saltwater interface below sandy beaches. J. Hydrol. 538, 563–573 (2016)
Nolle, L.: On a search strategy for collaborating autonomous underwater vehicles. In: Mendel 2015, 21st International Conference on Soft Computing, Brno, CZ, pp. 159–164 (2015)
Tholen, C., Nolle, L., Werner, J.: On the influence of localisation and communication error on the behaviour of a swarm of autonomous underwater vehicles. In: Mendel 2017, 23rd International Conference on Soft Computing, Brno, CZ (2017, to appear)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, vol. 4, pp. 1942−1948 (1995)
Bansal, J.C., Singh, P.K., Saraswat, M., Verma, A., Jadon, S.S., Abraham, A.: Inertia weight strategies in particle swarm optimization. In: Third World Congress on Nature and Biologically Inspired Computing, 19–21 October, Salamanca, Spain, pp. 633−640 (2011)
Wolfram, S.: Universality and complexity in cellular automata. Physica D: Nonlinear Phenom. 10(1–2), 1–35 (1984)
Tholen, C., Nolle, L., Zielinski, O.: On the effect of neighborhood schemes and cell shape on the behaviour of cellular automata applied to the simulation of submarine groundwater discharge. In: 31th European Conference on Modelling and Simulation, ECMS 2017, pp. 255–261 (2017)
Nolle, L., Thormählen, H., Musa, H.: Simulation of submarine groundwater discharge of dissolved organic matter using cellular automata. In: 30st European Conference on Modelling and Simulation, ECMS 2016, pp. 265–269 (2016)
Jiménez, A., Posadas, A.M., Marfil, J.M.: A probabilistic seismic hazard model based on cellular automata and information theory. Nonlinear Process. Geophys. 12(3), 381–396 (2005)
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence, pp. 69–73 (1998)
Faiçal, B.S., Pessin, G., Filho, G.P.R., Carvalho, A.C.P.L.F., Gomes, P.H., Ueyama, J.: Fine-tuning of UAV control rules for spraying pesticides on crop fields: an approach for dynamic environments. Int. J. Artif. Intell. Tools 25(1), 1660003-1–160003-19 (2016)
Družeta, S., Ivić, S.: Examination of benefits of personal fitness improvement dependent inertia for particle swarm optimization. Soft Comput. 21(12), 3387–3400 (2017)
Eberhart, R.C., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm optimization. In: 2000 Congress on Evolutionary Computation, vol. 1, pp. 84–88 (2000)
Bratton, D., Kennedy, J.: Defining a standard for particle swarm optimization. In: 2007 IEEE Swarm Intelligence Symposium, pp. 120–127 (2007)
Edwards, A.M., Phillips, R.A., Watkins, N.W., Freeman, M.P., Murphy, E.J., Afanasyev, V., Buldyrev, S.V., da Luz, M.G.E., Raposo, E.P., Stanley, H.E., Viswanathan, G.M.: Revisiting levy flight search patterns of wandering albatrosses, bumblebees and deer. Nature 449, 1044–1049 (2007)
Subha, R., Himavathi, S.: Acclerated particle swarm optimization algorithm for maximum power point tracking in partially shaded PV systems. In: 3rd International Conference on Electrical Energy Systems, pp. 232–236 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Tholen, C., Nolle, L. (2017). Parameter Search for a Small Swarm of AUVs Using Particle Swarm Optimisation. In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXIV. SGAI 2017. Lecture Notes in Computer Science(), vol 10630. Springer, Cham. https://doi.org/10.1007/978-3-319-71078-5_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-71078-5_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-71077-8
Online ISBN: 978-3-319-71078-5
eBook Packages: Computer ScienceComputer Science (R0)