# Week8-MOEAs - CS Dept dmathias/cs419/slides/Week8-MOEAs.pdfآ Week8-MOEAs Author: David Mathias...

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### Transcript of Week8-MOEAs - CS Dept dmathias/cs419/slides/Week8-MOEAs.pdfآ Week8-MOEAs Author: David Mathias...

10/20/19

1

Genetic Algorithms

Multi-objective Optimization

Multi-objective Evolutionary Algorithms

• Multi-objective optimization problems (MOPs) - Examples - Domination - Pareto optimality - Practical example

• EC approaches - Preference-based - Ideal

• Preserving diversity

Multi-Objective Problems (MOPs)

• Wide range of problems can be categorised by the presence of a number of n possibly conflicting objectives: – robotic path planning: – buying a car: speed vs. price vs. reliability – engineering design: lightness vs. strength

• Solving an MOP presents two problems: – finding set of good solutions – choice of best for particular application

Multi-objective problems Example: Path planning

source

destination

Goal: find a shortest, obstacle-avoiding path from source to destination.

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Multi-objective problems Example: Path planning

• What are the objectives? – Path length (minimize) – Obstacle collisions (minimize)

• Any others? – Number of waypoints (minimize) – Smoothness (minimize/maximize – depends on

definition) – Intermediate destinations?

Multi-objective problems Example: Path planning

source

destination

Conflicting objectives:

Optimal for path lengthOptimal for obstacle collisionsWhich is a better solution?

Multi-objective problems Example: Buying a car

cost

speed

Inexpensive but slow

Fast but expensive

Which is a better solution?

Multi-objective Optimization Problems Two spaces

Decision (variable) space

Objective space

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Multi-objective Optimization Problems Comparing Solutions

• Optimisation task: Minimize both f1 and f2

• Then: a is better than b a is better than c a is worse than e a and d are incomparable

Objective space

Multi-objective Optimization Problems The Dominance relation

• Solution X dominates solution Y, (X Y), if: – X is no worse than Y in every objective – X is better than Y in at least one objective

solutions dominated

by x

solutions dominating

x

∀" ∈ 1,… , ' () ≤ +), and ∃" ∈ 1,… , ' () < +)

Important note: above definition is for minimization problem. Reverse inequalities for maximization

Multi-objective Optimization Problems Origins of Pareto optimization

• Vilfredo Pareto (1848-1923) was an Italian economist, political scientist and philosopher

• For much of his life he was a political economist at the University of Lausanne (Switzerland)

• Manual of Political Economy (1906): described equilibrium for problems consisting of a system of objectives and constraints

• Pareto optimality(economics): an economy is is functioning optimally when no one’s position can be improved without someone else’s position being made worse

Multi-objective Optimization Problems Pareto optimality

• Solution x is non-dominated among a set of solutions Q if no solution from Q dominates x

• A set of non-dominated solutions from the entire feasible solution space is the Pareto-optimal set, its members Pareto-optimal solutions

• Pareto-optimal front: an image of the Pareto-optimal set in the objective space

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Multi-objective Optimization Problems Illustration of the concepts

f1(x)

f2(x) min

min

Multi-objective Optimization Problems Illustration of the concepts

f1(x)

f2(x) min

min

non-dominated solutions

Practical Example: The beam design problem

d

Minimize weight and deflection of a beam (Deb, 2001):

Practical Example: The beam design problem – Formal Definition

2

1

3

2 4

max 3

max

3

max

( , ) 4

64 ( , )

3 0.01 m 0.05 m 0.2 m 1.0 m

32

7800 kg/m , 2 kN 207 GPa 300 MPa, 0.005 m

y

y

df d l l

Plf d l E d

d l Pl S d

P E S

pr

d p

s p

d d

r

d

=

= =

£ £ £ £

= £

£

= = = = =

• Minimize

• minimize

• subject to

where

(beam weight)

(beam deflection)

(maximum stress)

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Practical Example: The beam design problem

Decision (variable) space Objective space

Feasible Solutions:

Practical Example: The beam design problem

Goal: Finding non-dominated solutions:

Goal of multi-objective optimizers

• Find a set of non-dominated solutions (approximation of the Pareto-optimal front) following the criteria of: – convergence (as close as possible to the Pareto-

optimal front) – diversity (spread, distribution)

Single vs. Multi-objective Optimization

Characteristic Singleobjective optimisation

Multiobjective optimisation

Number of objectives one more than one

Spaces single two: decision (variable) space, objective space

Comparison of candidate solutions

x is better than y x dominates y

Result one (or several equally good) solution(s)

Pareto-optimal set

Algorithm goals convergence convergence, diversity

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Multi-objective optimization Two approaches

• Preference-based: traditional, using single objective optimisation methods

• Ideal: possible with novel multiobjective optimisation techniques, enabling better insight into the problem

Multi-objective optimization Preference-based approach

• Given a multiobjective optimisation problem,

• use higher-level information on importance of objectives

• to transform the problem into a singleobjective one,

• then solve it with a single objective optimization method

• to obtain a particular trade-off solution.

Multi-objective optimization Preference-based approach

Modified problem: 1 1

( ) ( ), [0,1], 1x x M M

m m m m m m

F w f w w = =

= Î =å å

Hyperplanes in the objective space!

The weighted sum scalarizes the objective vector: we no have a single-objective problem

Multi-objective optimization Ideal approach

• Given a multiobjective optimization problem,

• solve it with a multi-objective optimization method

• to find multiple trade-off solutions,

• and then use higher-level information

• to obtain a particular trade-off solution.

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EC approach to multi-objective optimization:

Advantages

• Population-based nature of search means you can

simultaneously search for set of points approximating Pareto front

• Can return a set of trade-off solutions (approximation

set) in a single run

• Don’t have to make guesses about which combinations

of weights might be useful

• Makes no assumptions about shape of Pareto front - can

be convex / discontinuous etc.

EC approach to multi-objective optimization: Requirements

• Way of assigning fitness, – usually based on dominance

• Preservation of diverse set of points – similarities to multi-modal problems

• Remembering all the non-dominated points you have seen – usually using elitism or an archive

EC approach: Fitness assignment options

• Could use aggregating approach and change weights during evolution – no guarantees

• Different parts of population use different criteria – e.g. VEGA, but no guarantee of diversity

• Dominance – ranking or depth based – fitness related to whole population – Question: how to rank non-comparable solutions?

EC approach: Diversity maintenance

• Usually done by niching techniques such as: – fitness sharing – adding amount to fitness based on inverse distance to nearest

neighbour (minimisation) – (adaptively) dividing search space into boxes and counting

occupancy

• All rely on some distance metric in genotype / phenotype space

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EC approach: Remembering good solutions

• Could just use elitist algorithm – e.g. ( µ + l ) replacement

• Maintain an archive of non-dominated solutions – some algorithms use this as second population that can be in

recombination etc. – others divide archive into regions too, e.g. PAES

Multi-objective optimization Problem Summary

• MO problems occur very frequently

• EAs are very good at solving MO problems

• MOEAs are one of the most successful EC subareas