tudelft stramien 16_9_on_optimization

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On Evaluation & Optimization

A very short review of essential topics

Ir. Pirouz Nourian

PhD Candidate, Researcher & Instructor

@ AE+T/Chair of Design Informatics

@URBANISM/chair of Urban Design

MSc in Architecture 2009

BSc in Control Engineering 2005

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What is optimization all about?

• Goal-Oriented Search

• typically maximization or minimization

• Objective Function, Goal

• Performance Indicators

• Performance Optimization

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What is evaluation all about?

• Formulating an indicator that could

describe the performance of an

object/system according to:

– A concept of quality/fitness

– A frame of reference

– A benchmark

– An evaluation framework

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Analysis vs Evaluation

• Synthesis (conclusion) – Putting together various analyses

• Aggregation – Integral

– Sum

– Arithmetic Mean

– Harmonic Mean

– Geometric Mean

– Etcetera

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Aggregating Goals?

• Multi-Criteria Analysis vs Multi-Objective Optimization

• Weighting goals?

• Apples & Oranges

• Commensurability

• Dimensional Analysis

• WSM vs WPM in Decision Problems

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Terminology Problem Setting/Formulation

Suppose the design is formulated as a rectangle with the width W and height

H, which its area is desired to be maximized (Given the perimeter as a

constant P). In other words, the problem is to find the maximum rectangular

area that one can circumscribe with a rope of the length P. We have:

Constraint

𝑃 = 2 𝑊+ 𝐻 = 𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡

Design Variable

Either W or H can be considered as a variable parameter:

𝐸𝑖𝑡ℎ𝑒𝑟 𝐻 =(𝑃 − 2𝑊)

2 𝑜𝑟 𝑊 =

(𝑃 − 2𝐻)

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Objective (Fitness) Function

We can write the Area as a function of the single variable 𝑊 as below:

𝐴𝑟𝑒𝑎 𝑊 = 𝑊.𝐻 = 𝑊.𝑃 − 2𝑊

2= 𝑃𝑊/2 −𝑊2

Problem-Solving

𝐴𝑟𝑒𝑎′ 𝑊 = 𝑃/2 − 2𝑊

𝐿𝑒𝑡 𝐴𝑟𝑒𝑎′ 𝑊 =𝑃

2− 2𝑊 = 0

𝑦𝑖𝑒𝑙𝑑𝑠𝑊 = 𝑃/4 & 𝐻 = 𝑃/4

𝐴𝑟𝑒𝑎𝑚𝑎𝑥 = 𝑊.𝐻 = 𝑃2/16

Solution

Perimeter Given

Maximum Area? Desired

H

W

=

= /16

W=P/4

H=P/4

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The Importance of Formulation/Design

The maximum area achieved with a rectangle is equal to W. H = 𝑃2/16, whereas if the designer in question had chosen a circle, they would have achieved the following surface area:

𝐴 = 𝜋𝑟2, 𝑃 = 2𝜋𝑟 = 𝑐𝑜𝑛𝑠𝑡.𝑦𝑖𝑒𝑙𝑑𝑠𝐴 = 𝜋(

𝑃

2𝜋)2

=𝑃2

4𝜋>𝑃2

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Formulation of a Single-Objective

Optimization Problem Find a combination of the input variables that optimizes (minimizes/maximizes) a single outcome of a process:

minimize𝑥𝑓(𝑥)

Subject to:

𝑔 𝑥𝑖 ≤ 0, 𝑖 = 1,… ,𝑚 ℎ 𝑥𝑖 = 0, 𝑖 = 1,… , 𝑝

Where:

• 𝑓 𝑥 : ℝ𝑛 → ℝ is an objective function to be minimized (or maximized) over variable 𝑥,

• 𝑔 𝑥𝑖 ≤ 0 are constraints, and

• ℎ 𝑥𝑖 = 0 are equality constraints.

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Find a combination of the input variables that optimizes (minimizes/maximizes) a single outcome of a process:

minimize𝑥𝑓(𝑥)

Subject to:

𝑔 𝑥𝑖 ≤ 0, 𝑖 = 1,… ,𝑚 ℎ 𝑥𝑖 = 0, 𝑖 = 1,… , 𝑝

Where:

• 𝑓 𝑥 : ℝ𝑛 → ℝ is an objective function to be minimized (or maximized) over variable 𝑥,

• 𝑔 𝑥𝑖 ≤ 0 are constraints, and

• ℎ 𝑥𝑖 = 0 are equality constraints.

Formulation of a Single-Objective

Optimization Problem

Image Credit: http://www.turingfinance.com/fitness-landscape-analysis-for-computational-finance/

http://www.turingfinance.com/fitness-landscape-analysis-for-computational-finance/













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Formulation of a Multi-Objective

Optimization Problem Find a combination of the input variables that optimizes (minimizes/maximizes) multiple (different, independent, and often conflicting) outcomes of a process:

minimize𝑥[𝑓1 𝑥 , 𝑓 𝑥 ,… , 𝑓𝑘(𝑥)]

𝑠. 𝑡. 𝑥 ∈ 𝑋

Where:

• 𝑓: 𝑋 → ℝ𝑘, 𝑓 𝑥 = [𝑓1 𝑥 , 𝑓 𝑥 ,… , 𝑓𝑘(𝑥)]𝑇is a vector-valued objective function to

be minimized over variable𝑥 ∈ 𝑋. If an objective is to be maximized we negate it in the vector-valued objective function.

• Typically, there does not exist a solution optimal for all objectives; therefore we focus on Pareto-Optimal solutions; which are solutions that cannot be improved in any of the objectives without degrading at least one of the other objectives. Technically, a solution is called Pareto Optimal if not (Pareto) dominated, that is: – A feasible solution 𝑥1 ∈ 𝑋 is said to dominate another solution solution 𝑥 ∈ 𝑋 if:

– 𝑓𝑖 𝑥1 ≤ 𝑓𝑖 𝑥

for ∀𝑖 ∈ 1, 𝑘 ; and ∃𝑗 ∈ 1, 𝑘 such that 𝑓𝑗 𝑥1 < 𝑓𝑗 𝑥

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Optimization Problem Find a combination of the input variables that optimizes (minimizes/maximizes) multiple (different, independent, and often conflicting) outcomes of a process:

minimize𝑥[𝑓1 𝑥 , 𝑓 𝑥 ,… , 𝑓𝑘(𝑥)]

𝑠. 𝑡. 𝑥 ∈ 𝑋

Where:

• 𝑓: 𝑋 → ℝ𝑘, 𝑓 𝑥 = [𝑓1 𝑥 , 𝑓 𝑥 ,… , 𝑓𝑘(𝑥)]𝑇is a vector-valued objective function to

be minimized over variable𝑥 ∈ 𝑋. If an objective is to be maximized we negate it in the vector-valued objective function.

• Typically, there does not exist a solution optimal for all objectives; therefore we focus on Pareto-Optimal solutions; which are solutions that cannot be improved in any of the objectives without degrading at least one of the other objectives. Technically, a solution is called Pareto Optimal if not (Pareto) dominated, that is: – A feasible solution 𝑥1 ∈ 𝑋 is said to dominate another solution solution 𝑥 ∈ 𝑋 if:

– 𝑓𝑖 𝑥1 ≤ 𝑓𝑖 𝑥

for ∀𝑖 ∈ 1, 𝑘 ; and ∃𝑗 ∈ 1, 𝑘 such that 𝑓𝑗 𝑥1 < 𝑓𝑗 𝑥

Image Credits: (Left) Enginsoft: http://www.enginsoft.com/technologies/multidisciplinary-analysis-and-optimization/multiobjective-optimization/ (Right) Professor Peter J Fleming: https://www.sheffield.ac.uk/acse/staff/peter_fleming/intromo

http://www.enginsoft.com/technologies/multidisciplinary-analysis-and-optimization/multiobjective-optimization/










https://www.sheffield.ac.uk/acse/staff/peter_fleming/intromo

https://www.sheffield.ac.uk/acse/staff/peter_fleming/intromo

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Optimization Problem Find a combination of the input variables that optimizes (minimizes/maximizes) multiple (different,

independent, and often conflicting) outcomes of a process:

Image Courtesy of Ilya Loshchilov; http://www.loshchilov.com/publications.html

http://www.loshchilov.com/publications.html

http://www.loshchilov.com/publications.html

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Multiple Objectives into a Single One?

What if we want/have to find the single best solution?

Then we need to aggregate multiple objectives into one; but how?

Shall we make a weighted average of the objectives and seek to

optimize it?

Or…

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Dimensional Analysis

• 7even Fundamental Quantities in Physics

• Mass, Length, Time, Electric Current,

Absolute Temperature, Amount of

Substance, Luminous Intensity

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• 7even Fundamental Quantities in Physics

From The International System of Units (SI) [8th edition, 2006; updated in 2014]

SI: By convention physical quantities are organized in a system of dimensions. Each of the seven base quantities used in the SI is regarded as having its own dimension, which is symbolically represented by a single sans serif roman capital letter. The symbols used for the base quantities, and the symbols used to denote their dimension, are given as follows.

http://www.bipm.org/en/publications/si-brochure/section1-3.html

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Base quantities and dimensions used in the SI

Base quantity Symbol for quantity

Symbol for dimension

SI unit

mass m M Kilogram (kg)

length l, x, r, etc. L Meter (m)

time, duration t T Second (s)

electric current I, i l Ampere (A)

absolute temperature T Θ Kelvin (K)

amount of substance n N Mole (mol)

luminous intensity I v J Candela (cd)

https://en.wikipedia.org/wiki/SI_base_unit



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Base quantities and dimensions used in the SI

All other quantities are derived quantities, which may be written in terms of the base quantities by the equations of physics. The dimensions of the derived quantities are written as products of powers of the dimensions of the base quantities using the equations that relate the derived quantities to the base quantities. In general the dimension of any quantity Q is written in the form of a dimensional product,

dim𝑄 = 𝑀𝛼𝐿𝛽𝑇𝛾𝐼𝛿Θ 𝑁 𝐽 where the exponents 𝛼, 𝛽, 𝛾, 𝛿, 휀, 휁, and 휂, which are generally small integers which can be positive, negative or zero, are called the dimensional exponents. The dimension of a derived quantity provides the same information about the relation of that quantity to the base quantities as is provided by the SI unit of the derived quantity as a product of powers of the SI base units.

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Example: What is the dimension of Energy?

Mechanical Energy can be the work of a force along a displacement, that is found by the dot product of the two vectors as a scalar:

𝑊 = 𝑭.𝑫 While force can be described according to the Newton’s Second Law, as what is needed to accelerate a mass:

𝑭 = 𝑚𝒂 Where acceleration can be described in terms of changes in velocity of a moving object as below:

𝒂 =∆𝑽

∆𝑡

And velocity can be formulated as the rate of displacement over time:

𝑽 =∆𝒙

∆𝑡

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𝒂 =∆𝑽

∆𝑡


𝑽 =∆𝒙

∆𝑡⇒ 𝒅𝒊𝒎 𝑽 = 𝐿𝑇−1

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𝒂 =∆𝑽

∆𝑡⇒ 𝒅𝒊𝒎 𝑎 = 𝐿𝑇−


𝑽 =∆𝒙


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𝑭 = 𝑚𝒂 ⇒ 𝒅𝒊𝒎 𝑭 = 𝑀𝐿𝑇− Where acceleration can be described in terms of changes in velocity of a moving object as below:

𝒂 =∆𝑽



𝑽 =∆𝒙


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𝑊 = 𝑭.𝑫 ⇒ 𝒅𝒊𝒎 𝑊 = 𝑀𝐿 𝑇− While force can be described according to the Newton’s Second Law, as what is needed to accelerate a mass:

𝑭 = 𝑚𝒂 ⇒ 𝒅𝒊𝒎 𝑭 = 𝑀𝐿𝑇− Where acceleration can be described in terms of changes in velocity of a moving object as below:

𝒂 =∆𝑽



𝑽 =∆𝒙


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Therefore, the dimension of energy (in any form) is equal to the dimension of energy in mechanical form and equal to:

dim𝐸 = 𝑀𝐿 𝑇−

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Long Story Short: Apples & Oranges cannot be

compared (Added, Subtracted, Averaged)!

We can only compare (and thus add or subtract) quantities of the same dimension. It can be readily seen that we cannot get an average nor a weighted average of quantities of different physical dimensions, as that would entail adding incommensurate quantities.

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Apples & Oranges

Addition, Subtraction and Arithmetic Averages

are senseless for incommensurate quantities

We can only compare (and thus add or subtract) quantities of the same dimension. It can be readily seen that we cannot get an average nor a weighted average of quantities of different physical dimensions, as that would entail adding incommensurate quantities.

Image Credit: Paul Cézanne, Still Life with Apples and Oranges

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Combining Goals/Criteria

Weighted Sum Model & Weighted Product Model

For commensurate goals/criteria:

𝑥 = 𝑤𝑖𝑥𝑖𝑛𝑖=1

𝑤𝑖𝑛𝑖=1

or 𝑥 = 𝑤𝑖𝑥𝑖𝑛𝑖=1 if weights are normalized; i.e. 𝑤𝑖

𝑛𝑖=1 = 1

For incommensurate goals/criteria:

𝑥 = 𝑥𝑖𝑤𝑖𝑛

𝑖=1

1

𝑤𝑖𝑛𝑖=1 or 𝑥 = 𝑥𝑖

𝑤𝑖𝑛𝑖=1 if weights are normalized

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Combining Goals/Criteria

Fuzzy Aggregation

For goals that are fuzzifiable (those for which bounds or benchmarks are known): 𝑍𝑎𝑑𝑒ℎ 𝐴𝑁𝐷: 𝑥𝑖

𝑖

≔ min𝑖{𝑥𝑖}

𝑍𝑎𝑑𝑒ℎ 𝑂𝑅: 𝑥𝑖𝑖

≔ max𝑖{𝑥𝑖}

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Questions?

• Be careful with making claims about optimized designs • Remember that evaluation is not equal to analysis/simulation • Problem Formulation is more important than problem solving • Optimization is not a solution to all problems in design • All goals cannot be dealt with at once; as there is usually a hierarchy of

issues • A bad design cannot be corrected with optimization • Optimization is merely about searching within the possibilities created

by yourself; try to give rise to good possibilities.

THANKS FOR YOUR ATTENTION