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eXtended Multi-objective Differential Evolution with Spherical Pruning, < spMODEx > algorithm

version 1.0 (15.7 KB) by Gilberto Reynoso-Meza
An evolutionary algorithm for multi-objective optimization

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Updated 22 Nov 2017

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This code implements an eXtended version of the Multi-Objective Differential Evolution algorithms with spherical pruning (spMODE-II and spMODE algorithms). This toolbox will be updated with new mechanisms and features, according to the progress of our research on the topic.
This Toolset comprises of the following files:

1) RunTutorial.m
Runs and publish in html format the Tutorial.m file.

2) Tutorial.m
A quick reference tutorial for the spMODEx algorithm.

3) MOEAparam.m
Generates the required parameters to run the spMODEx algorithm.

4) spMODEx.m
Runs the optimization algorithm. This code implements a MOEA with different mechanisms, describen in the following papers:

** [Mechanism for pertinency improvement, initial release 2017] **
Reynoso-Meza, G., Sanchis, J., Blasco, X., & García-Nieto, S. (2014). Physical programming for preference driven evolutionary multi-objective optimization. Applied Soft Computing, 24, 341-362.

** [Mechanism for diversity improvement, initial release 2017] **
Reynoso-Meza G., Sanchis J., Blasco X., Martínez M. (2010) Design of Continuous Controllers Using a Multiobjective Differential Evolution Algorithm with Spherical Pruning. In: Di Chio C. et al. (eds) Applications of Evolutionary Computation. EvoApplications 2010. Lecture Notes in Computer Science, vol 6024. Springer, Berlin, Heidelberg

It is based on two previous versions (spMODE and spMODE-II) that were detailed in the following PhD thesis:
Gilberto Reynoso-Meza. Controller Tuning by Means of Evolutionary Multiobjective Optimization: a Holistic Multiobjective Optimization Design Procedure. PhD. Thesis (2014), Universitat Politècnica de València. Url: http://hdl.handle.net/10251/38248. Under the supervision of J.Sanchis and X.Blasco from CPOH/ai2/UPV:
http://cpoh.upv.es/

5) SphPruning.m
Implements the spherical pruning.

6) CostFuntion.m
The cost function to optimize

7) PhyIndex.m
The physical index for each objective vector.

Comments and Ratings (1)

jie wang

MATLAB Release Compatibility
Created with R2014a
Compatible with any release
Platform Compatibility
Windows macOS Linux

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