ev-MOGA Multiobjective Evolutionary Algorithm has been developed by the Predictive Control and Heuristic optimization Group at Universitat Politècnica de València. ev-MOGA is an elitist multi-objective evolutionary algorithm based on the concept of epsilon dominance. ev-MOGA, tries to obtain a good approximation to the Pareto Front in a smart distributed manner with limited memory resources. It also adjusts the limits of the Pareto front dynamically.
Details about ev-MOGA are described in (please, cite this algorithm as):
 M. Martínez, J.M. Herrero, J. Sanchis, X. Blasco and S. García-Nieto. Applied Pareto multi-objective optimization by stochastic solvers. Engineering Applications of Artificial Intelligence. Vol. 22 pp. 455 - 465, 2009 (ISSN:0952-1976).
The algorithm is also described in:
 J.M. Herrero, M. Martínez, J. Sanchis and X. Blasco. Well-Distributed Pareto Front by Using the epsilon-MOGA Evolutionary Algorithm. Lecture Notes in Computer Science, 4507, pp. 292-299, 2007. Springer-Verlag. (ISSN: 0302-9743)
ev-MOGA has been used in:
 J.M. Herrero, X. Blasco, M. Martínez, C. Ramos and J. Sanchis. Robust Identification of a Greenhouse Model using Multi-objective Evolutionary Algorithms. Biosystems Engineering. Vol. 98, Num. 3, pp. 335 - 346, Nov 2007. (ISSN 1537-5110)
 J.M. Herrero, X. Blasco , M. Martínez, J. Sanchis. Multiobjective Tuning of Robust PID Controllers Using Evolutionary Algorithms. Lecture Notes in Computer Science, 4974, pp. 515 - 524, 2008. Springer-Verlag. (ISSN: 0302-9743)
 J. M. Herrero, S. García-Nieto, X. Blasco, V. Romero-García, J. V. Sánchez-Pérez and L. M. Garcia-Raffi. Optimization of sonic crystal attenuation properties by ev-MOGA multiobjective evolutionary algorithm. Structural and Multidisciplinary Optimization. Vol. 39, num. 2, pp. 203 - 215, 2009 (ISSN:1615-1488).
 G. Reynoso, X. Blasco, J. Sanchis. Diseño Multiobjetivo de controladores PID para el Benchmark de Control 2008-2009. Revista Iberoamericana de Automática e Informática Industrial. Vol. 6, Num. 4, pp. 93 - 103 , 2009. (ISSN: 1697-7912)
 E. Afzalan, M. Joorabian. Emission, reserve and economic load dispatch problem with non-smooth and non-convex cost functions using epsilon-multi-objective genetic algorithm variable.
Electrical Power and Energy Systems 52 (2013) 55–67
The “ev-MOGAdescription.pdf” file contains the description of the ev-MOGA algorithm. You should read it before using the algorithm in order to understand how it works. Two multiobjective problems mop1.m and mop4.m are included as examples.
1) Create the Matlab function used to evaluate the objective functions (e.g. mop1.m)
2) Modify “run_evMOGA.m”. which contains the parameter configuration of the ev-MOGA and defines the optimization problem to solve.
3) Execute the script “run_evMOGA .m” to run the ev-MOGA. After execution, variables ParetoFront and ParetoSet variables are obtained in the workspace.