single-objective optimization for...

Single-ObjectiveOptimization for Architecture

Guilherme Ilunga

Instituto Superior Técnico, University of Lisbon

February 2019

Agenda

Agenda

• Introduction

Agenda

• Introduction

• Optimization

Agenda

• Introduction

• Optimization

• Automated Optimization Framework

Agenda

• Introduction

• Optimization


• Evaluation

Agenda

• Introduction

• Optimization


• Evaluation

• Conclusions

Introduction

Current Design Process

What we would like

What we would like

• Avoid repeating the process

What we would like


• Find the best variation of the design

What we would like


• Find the best variation of the design

• How?

The 3 stages of the process


Design


Design Analysis


Design Analysis Optimization

Making changes

Making changes

• The process requires changing the design

Making changes


• Complex designs require complex changes

Making changes


• Complex designs require complex changes

• How to make this easy?

Algorithmic Design (AD)

AD Tools: Grasshopper3D

AD Tools: Rosetta

Evaluating performance


• AD allows generating several variations of a design



• Which one is best?



• Which one is best?

• Analysis tools can be used

Analysis Tools: Radiance

Analysis Tools: Robot

Algorithmic Design and Analysis (ADA)

Which variations should be generated?


• ADA allows generating and evaluating variations



• Evaluations take time, should use acquired knowledge



• Evaluations take time, should use acquired knowledge

• Use optimization

What is optimization?

What is optimization?

𝑚𝑖𝑛𝑖𝑚𝑖𝑧𝑒 𝑓𝑖 𝑥 , 𝑖 = 1, 2, … , 𝐼

𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜 ℎ𝑗 𝑥 = 0, (𝑗 = 1, 2, … , 𝐽)

𝑔𝑘 𝑥 ≤ 0, (𝑘 = 1, 2,… , 𝐾)

Dissertation Objectives


• Study existing optimization algorithms



• Propose an automated optimization framework



• Propose an automated optimization framework

• Evaluate the framework with case studies

Optimization

Choosing an algorithm


• Hundreds of optimization algorithms



• How to choose among them?



• How to choose among them?

• Focus on their properties

Properties of algorithms


Single-Objective vs Multi-Objective



Deterministic vs Stochastic




Global vs Local




Global vs Local

Derivative-based vs Derivative-free

Derivative-free Optimization Algorithms


• Direct-search algorithms



• Metaheuristics



• Metaheuristics

• Model-based algorithms

DIRECT Algorithm

DIRECT Algorithm

Set centerpoint

DIRECT Algorithm

Set centerpoint

X

Y

DIRECT Algorithm

Subdivide dimensions

Set centerpoint

X

Y

DIRECT Algorithm


Set centerpoint

X

Y

X

Y

DIRECT Algorithm


Set centerpoint

`

X X

YY

DIRECT Algorithm


Set centerpoint

X X

YY

DIRECT Algorithm

Sample each hyperrectangle


Set centerpoint

X X

YY

DIRECT Algorithm



Set centerpoint

Y

XX X

YY

DIRECT Algorithm


Choose best hyperrectangle


Set centerpoint

Y

XX X

YY

DIRECT Algorithm




Set centerpoint

Y

X

Y

XX X

YY

DIRECT Algorithm




Set centerpoint

Y

X

Y

X

Y

XX

Y

Genetic Algorithm

Genetic Algorithm

Create Population

Genetic Algorithm

Create Population

Y

X

Genetic Algorithm

Create Population

X

Y

Genetic Algorithm

Create Population

Evaluate Population

X

Y

Genetic Algorithm

Create Population

Evaluate Population

Select Parents

X

Y

Genetic Algorithm

Create Population

Evaluate Population

Select Parents

X

Y

X

Y

Genetic Algorithm

Create Population

Evaluate Population

Select Parents

Y

X

Y

X

Genetic Algorithm

Create Population

Evaluate Population

Select Parents

Create Children

Y

X

Y

X

Genetic Algorithm

Create Population

Evaluate Population

Select Parents

Create Children

Y

X

Y

X

Y

X

Genetic Algorithm

Create Population

Evaluate Population

Select Parents

Create Children

Mutate Population

Y

X

Y

X

Y

X

Genetic Algorithm

Create Population

Evaluate Population

Select Parents

Create Children

Mutate Population

Y

X

Y

X

Y

X

Y

X

Genetic Algorithm

Create Population

Evaluate Population

Select Parents

Create Children

Mutate Population

Genetic Algorithm

CreatePopulation

Evaluate Population

Select Parents

Create Children

Mutate Population

• Random?• Gaussian?• Uniform?• …

• Random?• Best?• Tournament?• …

• One Point?• Two Point?• Uniform?• …

• Shuffle?• Gaussian?• Uniform?• …

Simple Surrogate Algorithm


Sample n points


Sample n points

Value

X


Sample n points

Update Surrogate

Value

X


Sample n points

Update Surrogate

Value

X

Value

X


Sample n points

Update Surrogate

Optimize Surrogate

Value

X

Value

X


Sample n points

Update Surrogate

Optimize Surrogate

Value

X

Value

X

Value

X


Sample n points

Update Surrogate

Optimize Surrogate

Evaluate k solutions

Value

X

Value

X

Value

X


Sample n points

Update Surrogate

Optimize Surrogate


Value

X

Value

X

Value

X

Value

X


Sample n points

Update Surrogate

Optimize Surrogate


Decrease Bounds

Value

X

Value

X

Value

X

Value

X


Sample n points

Update Surrogate

Optimize Surrogate


Decrease Bounds

Value

X

Value

X

Value

X

Value

X

Value

X

Automated Optimization Framework

Automated Optimization Process

Simple Code

Visualizing the result

Evaluation

Structural Optimization case study

Structural Optimization Results

Conclusions

Conclusions

• Optimization is useful for architecture

Conclusions


• The Genetic Algorithm is not a good choice

Conclusions


• The Genetic Algorithm is not a good choice

• The framework enables the usage of several algorithms

Contributions

Contributions

• Caetano, I., Ilunga, G., Belém, C., Aguiar, R., Feist, S., Bastos, F., & Leitão, A. (2018). Case Studies on the Integration of Algorithmic Design Processes in Traditional Design Workflows. 23rd CAADRIA Conference, Beijing, China.

• Ilunga, G., & Leitão, A. (2018). Derivative-free Methods for Structural Optimization. 36th eCAADe Conference, Lodz, Poland.

Questions?

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