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ev-MOGA Multiobjective Evolutionary Algorithm

version (315 KB) by Juan M. Herrero
This toolbox implements the ev-MOGA Multiobjective Evolutionary Algorithm.


Updated 27 Jul 2017

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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):
[1] 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:
[2] 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:
[3] 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)
[4] 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)
[5] 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).
[6] 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)

[7] 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

Basic instructions

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. The "ev-MOGA_UserGuide.pdf" file contains a brief description of how to use this toolbox and three illustrative examples.

Comments and Ratings (2)


bug, in version 1.7, corrected.
A new example (example 4.m) is added to show new functionality included in version 1.7

Code improvements. Initial population P can be initialized by means of the eMOGA.subpobIni and eMOGA.subpobIni_obj variables.

- Bug fixed (anomalous behaviour in first generation when there is only one individual in the pareto front).
- Minimal set of parameter simplified and some names changed to improve understandability.
- User guide revised.

Bug fixed

The description has been updated

Bug fixed and code improvements.
User interface improvements

An article which uses evMOGA has been added.

ev-MOGA toolbox.pdf file has been modified.

MATLAB Release Compatibility
Created with R14SP3
Compatible with any release
Platform Compatibility
Windows macOS Linux

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