Abstract
One of the main sources of inspiration for techniques applicable to complex search space and optimisation problems is nature. This paper introduces a new metaheuristic—Dispersive Flies Optimisation (DFO)—whose inspiration is beckoned from the swarming behaviour of flies over food sources in nature. The simplicity of the algorithm facilitates the analysis of its behaviour. A series of experimental trials confirms the promising performance of the optimiser over a set of benchmarks, as well as its competitiveness when compared against three other well-known population based algorithms. The convergence-independent diversity of DFO algorithm makes it a potentially suitable candidate for dynamically changing environment. In addition to diversity, the performance of the newly introduced algorithm is investigated using the three performance measures of accuracy, efficiency and reliability and its outperformance is demonstrated in the paper. Then the proposed swarm intelligence algorithm is used as a tool to identify microcalcifications on the mammographs. This algorithm is adapted for this particular purpose and its performance is investigated by running the agents of the swarm intelligence algorithm on sample mammographs whose status have been determined by the experts. Two modes of the algorithms are introduced in the paper, each providing the clinicians with a different set of outputs, highlighting the areas of interest where more attention should be given by those in charge of the care of the patients.
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
Notes
- 1.
The source code can be downloaded from the following page:
References
M.M. al-Rifaie, Dispersive flies optimisation, in Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, Annals of Computer Science and Information Systems, vol. 2, ed. by M. Ganzha, L. Maciaszek, M. Paprzycki. IEEE (2014), pp. 529–538. http://dx.doi.org/10.15439/2014F142
C. Beam, D. Sullivan, P. Layde, Effect of human variability on independent double reading in screening mammography. Acad. Radiol. 3(11), 891–897 (1996)
D. Bratton, J. Kennedy, Defining a standard for particle swarm optimization, in Proceedings of the Swarm Intelligence Symposium. (IEEE, Honolulu, 2007), pp. 120–127
R. Brem, J. Baum, M. Lechner, S. Kaplan, S. Souders, L. Naul, J. Hoffmeister, Improvement in sensitivity of screening mammography with computer-aided detection: a multiinstitutional trial. Am. J. Roentgenol. 181(3), 687–693 (2003)
A. Burgess, On the noise variance of a digital mammography system. Med. Phys. 31, 1987–1995 (2004)
E. Burnside, E. Sickles, R. Sohlich, K. Dee, Differential value of comparison with previous examinations in diagnostic versus screening mammography. Am. J. Roentgenol. 179(5), 1173–1177 (2002)
D. Chakraborty, Maximum likelihood analysis of free-response receiver operating characteristic (froc) data. Med. Phys. 16, 561 (1989)
M. Dorigo, M. Birattari, T. Stutzle, Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28–39 (2006)
D. Gehlhaar, D. Fogel, Tuning evolutionary programming for conformationally flexible molecular docking, in Evolutionary Programming V: Proceedings of the Fifth Annual Conference on Evolutionary Programming (1996), pp. 419–429
D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning (Addison-Wesley Longman Publishing Co., Inc., Boston, 1989)
J. Kennedy, R.C. Eberhart, Particle swarm optimization, in Proceedings of the IEEE International Conference on Neural Networks. vol. IV. (IEEE Service Center, Piscataway, 1995), pp. 1942–1948
C.Y. Lee, X. Yao, Evolutionary programming using mutations based on the Lévy probability distribution. IEEE Trans. Evolut. Comput. 8(1), 1–13 (2004)
O. Olorunda, A.P. Engelbrecht, Measuring exploration/exploitation in particle swarms using swarm diversity, in IEEE Congress on Evolutionary Computation. CEC 2008. (IEEE World Congress on Computational Intelligence). (IEEE, 2008), pp. 1128–1134
J. Peña, Theoretical and empirical study of particle swarms with additive stochasticity and different recombination operators, in Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation. GECCO’08. ACM, New York (2008), pp. 95–102, http://doi.acm.org/10.1145/1389095.1389109
P.N. Suganthan, N. Hansen, J.J. Liang, K. Deb, Y.P. Chen, A. Auger, S. Tiwari, Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Technical report, Nanyang Technological University, Singapore and Kanpur Genetic Algorithms Laboratory, IIT Kanpur (2005)
J. Sumkin, D. Gur, Computer-aided detection with screening mammography: improving performance or simply shifting the operating point? Radiology 239(3), 916–918 (2006)
X. Yao, Y. Liu, G. Lin, Evolutionary programming made faster. IEEE Trans. Evol. Comput. 3(2), 82–102 (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
al-Rifaie, M.M., Aber, A. (2016). Dispersive Flies Optimisation and Medical Imaging. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. Studies in Computational Intelligence, vol 610. Springer, Cham. https://doi.org/10.1007/978-3-319-21133-6_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-21133-6_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-21132-9
Online ISBN: 978-3-319-21133-6
eBook Packages: EngineeringEngineering (R0)