Leveldiagrams for multiobjective decision making

A tool to draw Leveldiagrams for Pareto Set/Front, allowingsome basics graphics manipulations.

NEW!! Complete rebuilt of the interactive tool for Level diagrams at:
https://es.mathworks.com/matlabcentral/fileexchange/62224-interactive-tool-for-decision-making-in-multiobjective-optimization-with-level-diagrams

This toolbox draws Leveldiagrams for Pareto Set and its associated Front, and allows some basics graphics manipulations (coloring plot according preferences, axis manipulation and point selections) to help in decision making. Details about Leveldiagrams and some examples supplied in this toolbox are described in:
[1] X. Blasco, J.M. Herrero, J. Sanchis, M. Martínez. A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization. Information Sciences 178 (2008) 3908–3924. doi:10.1016/j.ins.2008.06.010
Short video with example usage: http://politube.upv.es/play.php?vid=54412
For advance features comparing Pareto Fronts:

http://www.mathworks.es/matlabcentral/fileexchange/39458-level-diagrams-for-multiobjective-decision-making-and-design-concepts-comparison

Level diagram have been used in:

[2] E. Zio, R. Bazzo. Multiobjective optimization of the inspection intervals of a nuclear safety system: A clustering-based framework for reducing the Pareto Front. Annals of Nuclear Energy 37 (2010) 798–812. doi:10.1016/j.anucene.2010.02.020

[3] E. Zio, R. Bazzo. A clustering procedure for reducing the number of representative solutions in the Pareto Front of multiobjective optimization problems. European Journal of Operational Research
Volume 210, Issue 3, 1 May 2011, pages 624-634.
doi:10.1016/j.ejor.2010.10.021

[4] E. Zio, R. Bazzo. Level Diagrams analysis of Pareto Front for multiobjective system redundancy allocation. Reliability Engineering and System Safety, volume 96 (2011), pages 569–580. doi:10.1016/j.ress.2010.12.016

[5] A. Hajiloo, N. Nariman-zadeh, Ali Moeini, Pareto optimal robust design of fractional-order PID controllers for systems with probabilistic uncertainties. Mechatronics (ISSN 0957-4158). Online 11 May 2012. doi:10.1016/j.mechatronics.2012.04.003.

[6] A.T.D. Perera, R.A. Attalage, K.K.C.K. Perera, V.P.C. Dassanayake. A hybrid tool to combine multi-objective optimization and multi-criterion decision making in designing standalone hybrid energy systems. Applied Energy, Vol 107 (2013), pages 412–425.
http://dx.doi.org/10.1016/j.apenergy.2013.02.049

[7] S. Pourzeynali, S. Salimi, H. Eimani Kalesar. Robust multi-objective optimization design of TMD control device to reduce tall building responses against earthquake excitations using genetic algorithms. Scientia Iranica Transaction A. (2013).
http://dx.doi.org/10.1016/j.scient.2012.11.015

[8] H. Li and H. Ding, "Modeling and multi-objective design optimization of quasi-continuous high magnetic field systems," Progress In Electromagnetics Research, Vol. 139, 353-372, 2013.
http://www.jpier.org/pier/pier.php?paper=13031601

[9] Porto, Miguel; Correia, Otilia; Beja, Pedro. “Optimization of Landscape Services under Uncoordinated Management by Multiple Landowners.” PLOS ONE, 9 (1). http://dx.doi.org/10.1371/journal.pone.0086001 JAN 17 2014

[10] An automated approach towards detecting complex behaviours in deep brain oscillations
Mace, M; Yousif, N; Naushahi, M; Abdullah-Al-Mamun, K; Wang, SY; Nandi, D; Vaidyanathan, R
JOURNAL OF NEUROSCIENCE METHODS, 224 66-78; http://dx.doi.org/10.1016/j.jneumeth.2013.11.019 MAR 15 2014