. Bra, Brainweb : Simulated brain database, 2013. URL http, 2013.

T. Moriarty, A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data, Medical Imaging IEEE Transactions on, vol.21, issue.3, pp.193-199, 2002.

&. Alihodzic and . Tuba, Adis Alihodzic & Milan Tuba. Improved Bat algorithm applied to multilevel image thresholding, The Scientific World Journal, 2014.

M. Ahmadi-asl and &. Seyed-alireza-seyedin, Active contour optimization using particle swarm optimizer, Information and Communication Technologies, pp.1522-1523, 2006.

. Bandyopadhyay, A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA, IEEE Transactions on Evolutionary Computation, vol.12, issue.3, 2008.
DOI : 10.1109/TEVC.2007.900837

. Beckers, Trails and U-turns in the selection of a path by the ant Lasius niger, Journal of Theoretical Biology, vol.159, issue.4, pp.397-415, 1992.
DOI : 10.1016/S0022-5193(05)80686-1

C. James and . Bezdek, Pattern recognition with fuzzy objective function algorithms

R. Chen and &. Zhang, Robust Image Segmentation Using FCM With Spatial Constraints Based on New Kernel-Induced Distance Measure, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.34, issue.4, pp.1907-1916, 2004.
DOI : 10.1109/TSMCB.2004.831165

. Chuang, Fuzzy c-means clustering with spatial information for image segmentation . computerized medical imaging and graphics, pp.9-15, 2006.

M. Clerc, Particle swarm optimization, 2010.
DOI : 10.1002/9780470612163

URL : https://hal.archives-ouvertes.fr/hal-01155162

A. Carlos and . Coello, A comprehensive survey of evolutionary-based multiobjective optimization techniques, Knowledge and Information systems, vol.1, issue.3, pp.269-308, 1999.

. Coello, Handling multiple objectives with particle swarm optimization, IEEE Transactions on Evolutionary Computation, vol.8, issue.3, pp.256-279, 2004.
DOI : 10.1109/TEVC.2004.826067

&. Cohen, . Cohen, D. Laurent, &. Cohen, and . Isaac-cohen, Finite-element methods for active contour models and balloons for 2-d and 3-d images. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.15, issue.11, pp.1131-1147, 1993.

&. Collette and . Siarry, Yann Collette & Patrick Siarry. Multiobjective Optimization : Principles and Case Studies, 2003.

. Cremers, A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape, International Journal of Computer Vision, vol.18, issue.9, pp.195-215, 2007.
DOI : 10.1007/s11263-006-8711-1

. Das, Swagatam Das, Ajith Abraham, & Amit Konar. Metaheuristic clustering, vol.178, 2009.

. Deneubourg, Probabilistic behaviour in ants: A strategy of errors?, Journal of Theoretical Biology, vol.105, issue.2, pp.259-271, 1983.
DOI : 10.1016/S0022-5193(83)80007-1

M. Dorigo, Optimization, learning and natural algorithms, 1992.

M. Dorigo and &. Gambardella, Ant colony system: a cooperative learning approach to the traveling salesman problem, IEEE Transactions on Evolutionary Computation, vol.1, issue.1, pp.53-66, 1997.
DOI : 10.1109/4235.585892

M. Dorigo, V. Maniezzo, and A. Colorni, Ant system: optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.26, issue.1, pp.29-41, 1996.
DOI : 10.1109/3477.484436

J. Dréo, A. Petrowski, D. Éric, P. Taillard, and . Siarry, Métaheuristiques pour l'optimisation dicile, Eyrolles, 2003.

M. Eigen, Ingo Rechenberg Evolutionsstrategie Optimierung technischer Systeme nach Prinzipien der biologishen Evolution, 1973.

A. El-dor, J. Lepagnot, A. Nakib, and P. Siarry, PSO-2S Optimization Algorithm for Brain MRI Segmentation, Genetic and Evolutionary Computing, pp.13-22, 2014.
DOI : 10.1007/978-3-319-01796-9_2

M. Equihua, Fuzzy Clustering of Ecological Data, The Journal of Ecology, vol.78, issue.2, pp.519-534, 1990.
DOI : 10.2307/2261127

. Feng, Infrared image segmentation with 2-D maximum entropy method based on particle swarm optimization (PSO), Pattern Recognition Letters, vol.26, issue.5, pp.597-603, 2005.
DOI : 10.1016/j.patrec.2004.11.002

J. Lawrence, A. J. Fogel, . Owens, J. Michael, and . Walsh, Artificial intelligence through simulated evolution, 1966.

. Fonseca, Genetic Algorithms for Multiob- Références bibliographiques tion for [Graves & Pedrycz, 2010] Daniel Graves & Witold Pedrycz. Kernel-based fuzzy clustering and fuzzy clustering : A comparative experimental study. Fuzzy sets and systems, Mathematical Geology, vol.16, issue.1614, pp.283-301, 1984.

&. Gustafson, E. Kessel-donald, &. Gustafson, C. William, &. Kesselhandl et al., Fuzzy clustering with a fuzzy covariance matrix Exploiting the trade-o?the benefits of multiple objectives in data clustering, Decision and Control including the 17th Symposium on Adaptive Processes Evolutionary Multi-Criterion Optimization, pp.761-766, 1978.

&. Haralick, . Shapiro, M. Robert, &. Haralick, G. Linda et al., Image segmentation techniques, Technical Symposium East, pp. 2?9. International Society for Optics and Photonics, 1985.

L. Hegarat-mascle, A. Kallel, and X. Descombes, Ant Colony Optimization for Image Regularization Based on a Nonstationary Markov Modeling, IEEE Transactions on Image Processing, vol.16, issue.3, pp.865-878, 2007.
DOI : 10.1109/TIP.2007.891150

URL : https://hal.archives-ouvertes.fr/hal-00159093

. Held, Markov random field segmentation of brain MR images, IEEE Transactions on Medical Imaging, vol.16, issue.6, pp.878-886, 1997.
DOI : 10.1109/42.650883

&. Heppner and . Grenander, Frank Heppner & U Grenander. A stochastic nonlinear model for coordinated bird flocks, The ubiquity of chaos, pp.233-238, 1990.

H. John and . Holland, Adaptation in natural and artificial systems : An introductory analysis with applications to biology, control, and artificial intelligence, 1975.

]. Horng, Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation, Expert Systems with Applications, vol.38, issue.11, pp.13785-13791, 2011.
DOI : 10.1016/j.eswa.2011.04.180

R. Huang, Multiple kernel fuzzy clustering. Fuzzy Systems, IEEE Transactions on, vol.20, issue.1, pp.120-134, 2012.

. Huntsherger, Iterative fuzzy image segmentation, Pattern Recognition, vol.18, issue.2, pp.131-138, 1985.
DOI : 10.1016/0031-3203(85)90036-6

&. Ishibuchi and . Murata, Hisao Ishibuchi & Tadahiko Murata. A multi-objective genetic local search algorithm and its application to flowshop scheduling. Systems, Man, and Cybernetics, Part C : Applications and Reviews, IEEE Transactions on, vol.28, issue.3, pp.392-403, 1998.

&. Izakian and . Abraham, Fuzzy C-means and fuzzy swarm for fuzzy clustering problem, Expert Systems with Applications, vol.38, issue.3, pp.1835-1838, 2011.
DOI : 10.1016/j.eswa.2010.07.112

. Ji, A fuzzy clustering algorithm with robust spatially constraint for brain MR image segmentation, 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp.202-209, 2014.
DOI : 10.1109/FUZZ-IEEE.2014.6891640

. Joonkim, MRF model based image segmentation using hierarchical distributed genetic algorithm, Electronics Letters, vol.34, issue.25, pp.2394-2395, 1998.

&. Kang and . Zhang, Jiayin Kang & Wenjuan Zhang. Fingerprint image segmentation using modified fuzzy c-means algorithm, In Bioinformatics and Biomedical Engineering, pp.1-4, 2009.

. Kanungo, An ecient k-means clustering algorithm : Analysis and implementation. Pattern Analysis and Machine Intelligence, IEEE Transactions, issue.7, pp.24-881, 2002.

. Kapur, A new method for gray-level picture thresholding using the entropy of the histogram. Computer vision, graphics, and image processing, pp.273-285, 1985.

&. Karasulu, &. Korukoglu-]-bahadir-karasulu, and . Korukoglu, A simulated annealing-based optimal threshold determining method in edge-based segmentation of grayscale images, Applied Soft Computing, vol.11, issue.2, pp.2246-2259, 2011.
DOI : 10.1016/j.asoc.2010.08.005

L. Khodja, Contribution à la classification floue non supervisée, 1997.

. Kim, A genetic algorithm-based segmentation of Markov random field modeled images, IEEE Signal Processing Letters, vol.7, issue.11, pp.7-301, 2000.
DOI : 10.1109/97.873564

. Kirkpatrick, Optimization by Simulated Annealing, Science, vol.220, issue.4598, pp.671-680, 1983.
DOI : 10.1126/science.220.4598.671

R. John and . Koza, Genetic programming : on the programming of computers by means of natural selection, 1992.

&. Krishnapuram, &. Keller-]-raghuram-krishnapuram, M. James, and . Keller, A possibilistic approach to clustering. Fuzzy Systems, IEEE Transactions on, vol.1, issue.2, pp.98-110, 1993.

&. Lai, &. Lai, and . Tseng, A Hybrid Approach Using Gaussian Smoothing and Genetic Algorithm for Multilevel Thresholding, International Journal of Hybrid Intelligent Systems, vol.1, issue.3-4, pp.143-152, 2004.
DOI : 10.3233/HIS-2004-13-403

&. Lakshmanan and . Derin, Sridhar Lakshmanan & Haluk Derin. Simultaneous parameter estimation and segmentation of Gibbs random fields using simulated annealing. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.11, issue.8, pp.799-813, 1989.

. Lepagnot, Brain cine MRI segmentation based on a multiagent algorithm for dynamic continuous optimization, 2011 IEEE Congress of Evolutionary Computation (CEC), pp.1695-1702, 2011.
DOI : 10.1109/CEC.2011.5949819

&. Liew, &. Liew, and . Hong-yan, An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation, IEEE Transactions on Medical Imaging, vol.22, issue.9, pp.1063-1075, 2003.
DOI : 10.1109/TMI.2003.816956

. Liu, Adaptive image segmentation by using mean-shift and evolutionary optimisation, IET Image Processing, vol.8, issue.6, pp.327-333, 2014.
DOI : 10.1049/iet-ipr.2013.0195

. Lourenço, Iterated local search. arXiv preprint math, 2001.

&. Lu, &. Lu, and . Zhou, Image segmentation based on Markov random field with ant colony system, Robotics and Biomimetics IEEE International Conference on, pp.1793-1797, 2007.

&. Maceachern, A. Leonard, &. Maceachern, and . Tajinder-mankumakropoulos, Genetic algorithms for active contour optimization Madhubanti Maitra & Amitava Chatterjee. A hybrid cooperative? comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding, Circuits and Systems, 1998. ISCAS'98. Proceedings of the 1998 IEEE International Symposium on, pp.229-232, 1998.

R. Alice, &. Malisia, R. Hamid, and . Tizhoosh, Image thresholding using ant colony optimization, Computer and Robot Vision The 3rd Canadian Conference on, pp.26-26, 2006.

. Manousakas, Split-and-Merge Segmentation of Magnetic Resonance Medical Images: Performance Evaluation and Extension to Three Dimensions, Computers and Biomedical Research, vol.31, issue.6, pp.393-412, 1998.
DOI : 10.1006/cbmr.1998.1489

&. Maulik and . Bandyopadhyay, Genetic algorithm-based clustering technique, Pattern Recognition, vol.33, issue.9, pp.1455-1465, 1996.
DOI : 10.1016/S0031-3203(99)00137-5

T. Mcinerney and &. Terzopoulos, Deformable models in medical image analysis: a survey, Medical Image Analysis, vol.1, issue.2, pp.91-108, 1996.
DOI : 10.1016/S1361-8415(96)80007-7

T. Mcinerney and &. Terzopoulos, T-snakes : Topology adaptive snakes. Medical image analysis, pp.73-91, 2000.

. Metropolis, Equation of state calculations by fast computing machines. The journal of chemical physics, pp.1087-1092, 1953.

N. Monmarché, Algorithmes de fourmis artificielles : applications à la classification et à l'optimisation, 2000.

A. Nakib, Conception de métaheuristiques d'optimisation pour la segmentation d'images. Application à des images biomédicales, 2008.

A. Nakib, H. Oulhadj, and P. Siarry, Image histogram thresholding based on multiobjective optimization, Signal Processing, vol.87, issue.11, pp.2516-2534, 2007.
DOI : 10.1016/j.sigpro.2007.04.001

A. Nakib, H. Oulhadj, and P. Siarry, Non-supervised image segmentation based on multiobjective optimization, Pattern Recognition Letters, vol.29, issue.2, pp.161-172, 2008.
DOI : 10.1016/j.patrec.2007.09.008

URL : https://hal.archives-ouvertes.fr/hal-00916873

K. Michael, &. Ng, C. Joyce, and . Wong, Clustering categorical data sets using tabu search techniques, Pattern Recognition, vol.35, issue.12, pp.2783-2790, 2002.

&. Niknam and . Amiri, An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis, Applied Soft Computing, vol.10, issue.1, pp.183-197, 2010.
DOI : 10.1016/j.asoc.2009.07.001

. Niknam, An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering, Journal of Zhejiang University-SCIENCE A, vol.10, issue.4, pp.512-519, 2009.
DOI : 10.1631/jzus.A0820196

N. Otsu, A Threshold Selection Method from Gray-Level Histograms, IEEE Transactions on Systems, Man, and Cybernetics, vol.9, issue.1
DOI : 10.1109/TSMC.1979.4310076

&. Ouadfel and . Batouche, Salima Ouadfel & Mohamed Batouche. Ant colony system with local search for Markov random field image segmentation, Image Processing, 2003.

L. Dzung and . Pham, Fuzzy clustering with spatial constraints, Image Processing . 2002. Proceedings. 2002 International Conference on, p.65, 2002.

&. Pham, . Prince, L. Dzung, &. Pham, L. Jerry et al., An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities, Pattern Recognition Letters, vol.20, issue.1, pp.57-68, 1999.
DOI : 10.1016/S0167-8655(98)00121-4

L. Dzung, C. Pham, . Xu, L. Jerry, and . Prince, Current methods in medical image segmentation 1. Annual review of biomedical engineering, pp.315-337, 2000.

W. Craig and . Reynolds, Flocks, herds and schools : A distributed behavioral model, ACM SIGGRAPH Computer Graphics, vol.21, issue.4, pp.25-34, 1987.

J. Brian, W. Ritzel, and . Eheart, Using genetic algorithms to solve a multiple objective groundwater pollution containment problem

&. Sathya, . Kayalvizhi, &. Pd-sathya, and . Kayalvizhi, PSO-Based Tsallis Thresholding Selection Procedure for Image Segmentation, International Journal of Computer Applications, vol.5, issue.4, pp.39-46, 2010.
DOI : 10.5120/903-1279

D. Schaer, Multiple objective optimization with vector evaluated genetic algorithms, Proceedings of the 1st international Conference on Genetic Algorithms, pp.93-100, 1985.

A. James and . Sethian, A fast marching level set method for monotonically advancing fronts Mehmet Sezgin & Bulent Sankur. Survey over image thresholding techniques and quantitative performance evaluation, Proceedings of the National Academy of Sciences, pp.1591-1595, 1996.

W. Snyder, G. Bilbro, A. Logenthiran, and S. Rajala, Optimal thresholding???A new approach, Pattern Recognition Letters, vol.11, issue.12, pp.803-809, 1990.
DOI : 10.1016/0167-8655(90)90034-Y

&. Srinivas and . Deb, Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms, Evolutionary Computation, vol.27, issue.3, pp.221-248, 1994.
DOI : 10.1162/evco.1994.2.3.221

G. Storvik, A Bayesian approach to dynamic contours through stochastic sampling and simulated annealing. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.16, issue.10, pp.976-986, 1994.

. Szilagyi, MR brain image segmentation using an enhanced fuzzy C-means algorithm, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439), pp.724-726, 2003.
DOI : 10.1109/IEMBS.2003.1279866

]. Talbi, Metaheuristics : from design to implementation, 2009.
DOI : 10.1002/9780470496916

URL : https://hal.archives-ouvertes.fr/hal-00750681

. Tang, An Image Segmentation Algorithm Based on the Simulated Annealing and Improved Snake Model, 2007 International Conference on Mechatronics and Automation, pp.3876-3881, 2007.
DOI : 10.1109/ICMA.2007.4304194

. Tao, Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm, Pattern Recognition Letters, vol.24, issue.16, pp.3069-3078, 2003.
DOI : 10.1016/S0167-8655(03)00166-1

D. Klaus and . Toennies, Guide to Medical Image Analysis : Methods and Algorithms, 2012.

&. Tolias and . Panas, Yannis A Tolias & Stavros M Panas. Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions, 1998.

. Tsai, Recent Development of Metaheuristics for Clustering, Mobile, Ubiquitous, and Intelligent Computing, pp.629-636, 2014.
DOI : 10.1007/978-3-642-40675-1_93

. Tseng, Active contour model via multi-population particle swarm optimization, Expert Systems with Applications, vol.36, issue.3, pp.5348-5352, 2009.
DOI : 10.1016/j.eswa.2008.06.114

L. Tseng, &. Tseng, and . Lai, A genetic algorithm for MRF-based segmentation of multi-spectral textured images, Pattern Recognition Letters, vol.20, issue.14, pp.1499-1510, 1999.
DOI : 10.1016/S0167-8655(99)00117-8

. Wang, Ant colony optimization with active contour models for image segmentation, Control Theory & Applications, vol.4, p.4, 2006.

. Wu, Fuzzy c-means clustering algorithm based on kernel method, Computational Intelligence and Multimedia Applications ICCIMA 2003. Proceedings. Fifth International Conference on, pp.49-54, 2003.

A. Lotfi and . Zadeh, Fuzzy sets, Information and control, vol.8, issue.3, pp.338-353, 1965.

&. Zhang and . Chen, Daoqiang Zhang & Songcan Chen. Fuzzy clustering using kernel method, The 2002 International Conference on Control and Automation, 2002.

. Zhang, Texture feature fusion with neighborhood oscillating tabu search for high resolution image classification Photogrammetric Engineering &amp ; Remote Sensing, p.74, 2008.

]. and J. Zhang, A survey on evaluation methods for image segmentation, Pattern Recognition, vol.29, issue.8, pp.1335-1346, 1996.
DOI : 10.1016/0031-3203(95)00169-7

]. Zhang, Advances in image and video segmentation, 2006.
DOI : 10.4018/978-1-59140-753-9

. Zhao, Improved Image Thresholding Using Ant Colony Optimization Algorithm, 2008 International Conference on Advanced Language Processing and Web Information Technology, pp.210-215, 2008.
DOI : 10.1109/ALPIT.2008.105