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mocell.html
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<!DOCTYPE html>
<html lang="en">
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<title>jMetal Web site</title>
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<header id="header">
<h1 id="logo"><a href="index.html" title="index"><img src="images/jMetalLogo.png" alt="jMetal Logo" /></a></h1>
<p>
A Framework for Multi-Objective Optimization
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<a href="mocell.html">MOCell</a>
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<a href="mochc.html">MOCHC</a>
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</nav>
<article class="columms_container">
<header id="articleHeader">
<h1><strong>MOCell</strong></h1>
</header>
<!--<hr>-->
<aside>
<header>
</header>
<section>
<h2 class=invert>Neigborhoods in a cGA</h2>
<br />
<p>
Main feature of a cGA: solution diffusion through neigborhood overlapping
</p>
<img src="resources/cGA.png" alt="cGA" class="asideImage">
<h2 class=invert>Reproduction steps in a cGA</h2>
<br />
<p>
For each cell, the seleccion of parents is contrained to the cell's neigborhood
</p>
<img src="resources/cGABreedingLoop.png" alt="cGA Breeding Loop" class="asideImage">
<h2 class=invert>Pseudocode of a cGA</h2>
<br />
<img src="resources/cGACode.png" alt="cGA Breeding Loop" class="asideImage expand">
<h2 class=invert>Pseudocode of MOCell</h2>
<br />
<img src="resources/MOCEllCode.png" alt="cGA Breeding Loop" class="asideImage expand">
</section>
</aside>
<section class="col2_3">
<header>
<h1>Description</h1>
</header>
<p>
<strong>MOCell</strong> (MultiObjective Cellular genetic algorithm) is a family of cellular genetic algorithms (cGAs) for
multi-objective optimization which are the result of combining different strategies (synchronous vs asynchronous and two archive
feedback schemes).
</p>
<header>
<h1>Main features</h1>
</header>
<ul>
<li>
Adaptation of the canonical cGA scheme to deal with multi-objective problems
</li>
<li>
External archive to store the non-dominated solutions
<ul>
<li>
Density estimator: crowding distance
</li>
<li>
Feedback: the solutions of the archive are used to enhance the search
</li>
</ul>
</li>
<!--
<li>
Synchronous cGA: similar to the concept of generational genetic algorithm
</li> -->
<li>
Neighbohood: 1 hop neighbours (8 surrounding solutions)
</li>
<li>
Operators: SBX crossover and polynomial mutation
</li>
</ul>
<header>
<h1>Configurations</h1>
</header>
<p>
Six configurations of MOCell have been proposed:
</p>
<ul>
<li>
sMOCell1: the original synchronous MOCell algorithm
</li>
<li>
sMOCell2: MOCell + archive feedback through parent
selection
</li>
<li>
aMOCell1: asynchronous MOCell
</li>
<li>
aMOCell2: aMOCell1 + archive feedback through parent
selection
</li>
<li>
aMOCell3: aMOCell1 + replacing the worst neighbor
</li>
<li>
aMOCell4: Combination of aMOCell2 and aMOCell3
</li>
</ul>
<p>
In PPSN 2009 we presented a variant of aMOCell3, named CellDE, which replaced the
SBX crossover and polynomial mutation by the differential evolution operators
</p>
<header><h1>MOCell steps</h1></header>
<p>
<img src="resources/MOCell.png" alt="MOCell" class="col23">
</p>
</section>
<section class="col1">
<header>
<h1>References</h1>
</header>
<ul>
<li>
A.J. Nebro, J.J. Durillo, F. Luna, B. Dorronsoro, E. Alba. MOCell:
A Cellular Genetic Algorithm for Multiobjective Optimization.
International Journal of Intelligent Systems. Vol.24, No. 7 (July 2009), pp. 726-746.
<a href="http://dx.doi.org/10.1002/int.20358">DOI</a>
<a href="resources/NDL09.bib">BIBTEX</a>
</li>
<li>
A.J. Nebro, J.J. Durillo, F. Luna, B. Dorronsoro, E. Alba Design Issues in a Multiobjective Cellular Genetic Algorithm.
Evolutionary Multi-Criterion Optimization. 4th International Conference, EMO 2007. Sendai/Matsushima, Japan, March 2007.
<a href="resources/NDL07.bib">BIBTEX</a>
</li>
<li>
J.J. Durillo, A.J. Nebro, F. Luna, E. Alba Solving Three-Objective Optimization Problems Using a new Hybrid Cellular Genetic Algorithm.
PPSN X. LNCS, Vol. Volume 5199/2008, pp: 661-670. Dortmund, September 2008.
<a href="http://dx.doi.org/10.1007/978-3-540-87700-4_66">DOI</a>
<a href="http://www.lcc.uma.es/~antonio/investigacion/articulos/DNL08.bib">BIBTEX</a>
</li>
</ul>
</section>
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