The Max-Min algorithm for maximising the spread of Pareto...

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The Max-Min algorithm for maximising the spread of Pareto fronts. Arturo Hernández Aguirre ERCOFTAC 2006 DESIGN OPTIMIZATION: METHODS AND APPLICATIONS April 5 – 7, 2006 Gran Canaria, Spain CENTRO DE INVESTIGACIÓN EN MATEMÁTICAS

Transcript of The Max-Min algorithm for maximising the spread of Pareto...

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The Max-Min algorithm for maximising thespread of Pareto fronts.

Arturo Hernández Aguirre

ERCOFTAC 2006DESIGN OPTIMIZATION:

METHODS AND APPLICATIONSApril 5 – 7, 2006

Gran Canaria, Spain

CENTRO DE INVESTIGACIÓN EN MATEMÁTICAS

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A joint work with:

Dr. Salvador Botello Rionda

Dr. David Greiner Sánchez

Sergio Ivván Valdéz Peña

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Index

Issues in spreading vectors on the Pareto front

How do (some) EMO algorithms promote thespreading of vectors on the Pareto front?

A view of Pareto front – Pareto set

The Max-Min Algorithm

Experiments

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Issues in spreading vectors alongthe Pareto front

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Two main goals of EMO algorithms

-Pareto front convergence.reach the true Pareto front

-Pareto front spread.- two components:

end vectors of the frontuniform distribution along the front

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Spread over the front

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Desired spreading over the front

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Two issues of spreading over the front

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A Spreading measure (Deb)

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How do EMO algorithms promotespreading on the Pareto front?

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NSGA-II Algorithm

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NSGA-II spreading strategy

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SPEA-2 Algorithm

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SPEA-2 spreading mechanism

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PAES, ISPAES, PASSSS Algorithms

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PAES, ISPAES, PASSSS spreading strategy

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Experiment: the spreading mechanisms

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A view ofPareto front – Pareto set

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A plane truss optimization problem

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The objective functions

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The constraint functions

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Pareto fronts and Pareto setsreturned by non-dominated sorting

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Pareto fronts and Pareto setsreturned by non-dominated sorting

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Relationship Pareto set – Pareto front

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MAX-MIN

The Max of Min distanceAlgorithm.

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MAX of the MIN of distances

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MAX-MINAlgorithm

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BASIC APPROACH TOenhance an EMO algorithm with MAXMIN

EE(µ+λ)Pt ~ initial populationRepeatλ ~ reproduction of µ padresλ ~ mutation of λ hijosP ~ determine Pareto SetPt+1 ~ MAXMIN(P)

Until termination

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EXPERIMENTS

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Function ZDT6. MaxMin vs NSGA-IIPopulation: 16 - 16

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Function ZDT6. MaxMin vs NSGA-IIPopulation: 32 - 32

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Bycicle frame design experiment

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Bycicle frame design experiment

Design front Reference front

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Simple supported plate

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Simple supported plate

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Final remarks

-Max Min is easy to program-We are working on a proof of theuniformity of the spread-Max Min is being compared to algorithmsbased on epsilon dominance

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Thank you !!

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