•Download as PPTX, PDF•

1 like•196 views

Slides presented at the course Software Testing at Polytechnique Montreal, 2016.

Report

Share

Report

Share

Machine Learning Tools and Particle Swarm Optimization for Content-Based Sear...

Machine Learning Tools and Particle Swarm Optimization for Content-Based Sear...Distinguished Lecturer Series - Leon The Mathematician

A new Hybrid Particle Swarm Optimization with
Variable Neighborhood Search for Solving
Unconstrained Global Optimization Problems
A new hybrid particle swarm optimization with variable neighborhood search fo...

A new hybrid particle swarm optimization with variable neighborhood search fo...Aboul Ella Hassanien

Machine Learning Tools and Particle Swarm Optimization for Content-Based Sear...

Machine Learning Tools and Particle Swarm Optimization for Content-Based Sear...Distinguished Lecturer Series - Leon The Mathematician

A new Hybrid Particle Swarm Optimization with
Variable Neighborhood Search for Solving
Unconstrained Global Optimization Problems
A new hybrid particle swarm optimization with variable neighborhood search fo...

A new hybrid particle swarm optimization with variable neighborhood search fo...Aboul Ella Hassanien

An Introduction To Applied Evolutionary Meta Heuristics

An Introduction To Applied Evolutionary Meta Heuristics

Optimization and particle swarm optimization (O & PSO)

Optimization and particle swarm optimization (O & PSO)

Particle Swarm Optimization: The Algorithm and Its Applications

Particle Swarm Optimization: The Algorithm and Its Applications

Particle swarm optimization

Particle swarm optimization

Particle Swarm Optimization Matlab code Using 50, 5000 Swarms

Particle Swarm Optimization Matlab code Using 50, 5000 Swarms

Machine Learning Tools and Particle Swarm Optimization for Content-Based Sear...

Machine Learning Tools and Particle Swarm Optimization for Content-Based Sear...

Particle swarm optimization on pacman game problem solving

Particle swarm optimization on pacman game problem solving

Particle swarm optimization

Particle swarm optimization

Whale optimizatio algorithm

Whale optimizatio algorithm

Particle Swarm Optimization by Rajorshi Mukherjee

Particle Swarm Optimization by Rajorshi Mukherjee

nature inspired algorithms

nature inspired algorithms

Particle Swarm Optimization

Particle Swarm Optimization

Grasshopper optimization algorithm

Grasshopper optimization algorithm

Estimating Species Divergence Times in RevBayes – iEvoBio 2014

Estimating Species Divergence Times in RevBayes – iEvoBio 2014

Particle Swarm Optimization

Particle Swarm Optimization

Particle swarm optimization

Particle swarm optimization

An Introduction to RevBayes and Graphical Models

An Introduction to RevBayes and Graphical Models

A new hybrid particle swarm optimization with variable neighborhood search fo...

A new hybrid particle swarm optimization with variable neighborhood search fo...

Ant Colony Optimization: The Algorithm and Its Applications

Ant Colony Optimization: The Algorithm and Its Applications

Particle Swarm Optimization

Particle Swarm Optimization

Pso notes

Pso notes

Swarm intelligence pso and aco

Swarm intelligence pso and aco

DriP PSO- A fast and inexpensive PSO for drifting problem spaces

DriP PSO- A fast and inexpensive PSO for drifting problem spaces

swarm pso and gray wolf Optimization.pdf

swarm pso and gray wolf Optimization.pdf

Metaheuristic Algorithms: A Critical Analysis

Metaheuristic Algorithms: A Critical Analysis

Particle swarm optimization

Particle swarm optimization

PSO-ACO-Presentation.pptx

PSO-ACO-Presentation.pptx

A REVIEW OF PARTICLE SWARM OPTIMIZATION (PSO) ALGORITHM

A REVIEW OF PARTICLE SWARM OPTIMIZATION (PSO) ALGORITHM

A Fast and Inexpensive Particle Swarm Optimization for Drifting Problem-Spaces

A Fast and Inexpensive Particle Swarm Optimization for Drifting Problem-Spaces

Pso kota baru parahyangan 2017

Pso kota baru parahyangan 2017

Enhanced abc algo for tsp

Enhanced abc algo for tsp

TEXT FEUTURE SELECTION USING PARTICLE SWARM OPTIMIZATION (PSO)

TEXT FEUTURE SELECTION USING PARTICLE SWARM OPTIMIZATION (PSO)

SI and PSO --Machine Learning

SI and PSO --Machine Learning

Two-Stage Eagle Strategy with Differential Evolution

Two-Stage Eagle Strategy with Differential Evolution

Master of Science Thesis Defense - Souma (FIU)

Master of Science Thesis Defense - Souma (FIU)

Eagle Strategy Using Levy Walk and Firefly Algorithms For Stochastic Optimiza...

Eagle Strategy Using Levy Walk and Firefly Algorithms For Stochastic Optimiza...

Engineering Optimisation by Cuckoo Search

Engineering Optimisation by Cuckoo Search

AN IMPROVED MULTIMODAL PSO METHOD BASED ON ELECTROSTATIC INTERACTION USING NN...

AN IMPROVED MULTIMODAL PSO METHOD BASED ON ELECTROSTATIC INTERACTION USING NN...

International Journal of Engineering Research and Development (IJERD)

International Journal of Engineering Research and Development (IJERD)

UNIT-5 Optimization (Part-1).ppt

UNIT-5 Optimization (Part-1).ppt

Slides presented at the 2018 IEEE International Conference on Software Quality, Reliability & Security in Lisbon, July 2018. Performance Analysis Using Automated Grouping Mechanisms - Conference Portuga...

Performance Analysis Using Automated Grouping Mechanisms - Conference Portuga...Francisco de Melo Jr

Presentation infonuagique/ cloud computing presentation

Presentation infonuagique/ cloud computing presentation

Nutri App

Nutri App

NeuroGames - TCC Mackenzie Univ

NeuroGames - TCC Mackenzie Univ

Honour Thesis Gabriel Alabarse - TCC Anhembi Morumbi

Honour Thesis Gabriel Alabarse - TCC Anhembi Morumbi

Automata research developed at Saint Mary's 2014

Automata research developed at Saint Mary's 2014

Performance Analysis Using Automated Grouping Mechanisms - Conference Portuga...

Performance Analysis Using Automated Grouping Mechanisms - Conference Portuga...

Comparative Framework for Education

Comparative Framework for Education

H2HC Tracing Profiling Debugging

H2HC Tracing Profiling Debugging

Dreams

Dreams

Run time

Run time

Apresentação TDC Floripa 2014

Apresentação TDC Floripa 2014

Projeto mpx

Projeto mpx

Abortion Clinic In Pretoria ](+27832195400*)[ 🏥 Safe Abortion Pills in Pretoria | Abortions Clinic | Quality & Affordable Healthcare Services 🏥🚑!!Abortion Clinic In Pretoria ](+27832195400*)[ 🏥 Safe Abortion Pills in Pretor...

Abortion Clinic In Pretoria ](+27832195400*)[ 🏥 Safe Abortion Pills in Pretor...Medical / Health Care (+971588192166) Mifepristone and Misoprostol tablets 200mg

Abortion Pill Prices Mthatha (](+27832195400*)[ 🏥 Women's Abortion Clinic In Mthatha ( Eastern Cape ● Abortion Pills For Sale In Mthatha ● Mthatha 🏥🚑!!Abortion Pill Prices Mthatha (@](+27832195400*)[ 🏥 Women's Abortion Clinic In...

Abortion Pill Prices Mthatha (@](+27832195400*)[ 🏥 Women's Abortion Clinic In...Medical / Health Care (+971588192166) Mifepristone and Misoprostol tablets 200mg

Transformer Neural Network Use Cases with Links

Transformer Neural Network Use Cases with Links

From Knowledge Graphs via Lego Bricks to scientific conversations.pptx

From Knowledge Graphs via Lego Bricks to scientific conversations.pptx

Auto Affiliate AI Earns First Commission in 3 Hours..pdf

Auto Affiliate AI Earns First Commission in 3 Hours..pdf

Weeding your micro service landscape.pdf

Weeding your micro service landscape.pdf

The Evolution of Web App Testing_ An Ultimate Guide to Future Trends.pdf

The Evolution of Web App Testing_ An Ultimate Guide to Future Trends.pdf

Novo Nordisk: When Knowledge Graphs meet LLMs

Novo Nordisk: When Knowledge Graphs meet LLMs

CERVED e Neo4j su una nuvola, migrazione ed evoluzione di un grafo mission cr...

CERVED e Neo4j su una nuvola, migrazione ed evoluzione di un grafo mission cr...

Entropy, Software Quality, and Innovation (presented at Princeton Plasma Phys...

Entropy, Software Quality, and Innovation (presented at Princeton Plasma Phys...

[GeeCON2024] How I learned to stop worrying and love the dark silicon apocalypse

[GeeCON2024] How I learned to stop worrying and love the dark silicon apocalypse

Microsoft365_Dev_Security_2024_05_16.pdf

Microsoft365_Dev_Security_2024_05_16.pdf

Abortion Clinic In Pretoria ](+27832195400*)[ 🏥 Safe Abortion Pills in Pretor...

Abortion Clinic In Pretoria ](+27832195400*)[ 🏥 Safe Abortion Pills in Pretor...

Abortion Pills For Sale WhatsApp[[+27737758557]] In Birch Acres, Abortion Pil...

Abortion Pills For Sale WhatsApp[[+27737758557]] In Birch Acres, Abortion Pil...

architecting-ai-in-the-enterprise-apis-and-applications.pdf

architecting-ai-in-the-enterprise-apis-and-applications.pdf

Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit Milan

Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit Milan

BusinessGPT - Security and Governance for Generative AI

BusinessGPT - Security and Governance for Generative AI

Modern binary build systems - PyCon 2024

Modern binary build systems - PyCon 2024

Prompt Engineering - an Art, a Science, or your next Job Title?

Prompt Engineering - an Art, a Science, or your next Job Title?

Software Engineering - Introduction + Process Models + Requirements Engineering

Software Engineering - Introduction + Process Models + Requirements Engineering

OpenChain Webinar: AboutCode and Beyond - End-to-End SCA

OpenChain Webinar: AboutCode and Beyond - End-to-End SCA

Abortion Pill Prices Mthatha (@](+27832195400*)[ 🏥 Women's Abortion Clinic In...

Abortion Pill Prices Mthatha (@](+27832195400*)[ 🏥 Women's Abortion Clinic In...

- 1. Metaheuristic Algorithms for Optimization Francisco de Melo jr
- 2. Problem Introduction Research Question Meta heuristics Comparative evaluation Conclusion References Outline
- 3. Problem Generate data to test a program <non linear function>
- 4. Solution Consider the problem a minimization vs maximization problem We can apply Simplex! (From Linear programing)
- 5. Solution Simplex! From Linear programing Well, not quite
- 6. Research Question How to optimize the generation? Compare the several way to generate the data?
- 7. Introduction Meta heuristic algorithms* To solve the optimization problem (1), efficient search or optimization algorithms are needed. There are many optimization algorithms which can be classified in many ways, depending on the focus and characteristics. Name controversy*
- 8. Meta-heuristics Components Two major components of any metaheuristic algorithms are: intensification and diversification, or exploitation and exploration (Blum and Roli, 2003).
- 9. Meta heuristic I Particle Swarm Optimization PSO searches the space of an objective function by adjusting the trajectories of individual agents, called particles. Each particle traces a piecewise path which can be modelled as a time-dependent positional vector. The movement of a swarming particle consists of two major components: a stochastic component and a deterministic component. Each particle is attracted toward the position of the current global best g∗ and its own best known location x∗i , while exhibiting at the same time a tendency to move randomly.
- 11. Meta heuristic I Algorithm for each particle i = 1, ..., S do Initialize the particle's position with a uniformly distributed random vector: xi ~ U(blo, bup) Initialize the particle's best known position to its initial position: pi ← xi if f(pi) < f(g) then update the swarm's best known position: g ← pi Initialize the particle's velocity: vi ~ U(-|bup-blo|, |bup-blo|) while a termination criterion is not met do: for each particle i = 1, ..., S do for each dimension d = 1, ..., n do Pick random numbers: rp, rg ~ U(0,1) Update the particle's velocity: vi,d ← ω vi,d + φp rp (pi,d-xi,d) + φg rg (gd-xi,d) Update the particle's position: xi ← xi + vi if f(xi) < f(pi) then Update the particle's best known position: pi ← xi if f(pi) < f(g) then Update the swarm's best known position: g ← pi
- 13. Meta heuristic I After finding the two best values, the particle updates its velocity and positions with following equation (a) and (b). Fitness function update v[] = v[] + c1 * rand() * (pbest[] - present[]) + c2 * rand() * (gbest[] - present[]) (a) present[] = persent[] + v[] (b)
- 14. Five principles of swarm intelligence First is the proximity principle: the population should be able to carry out simple space and time computations. Second is the quality principle: the population should be able to respond to quality factors in the environment. Third is the principle of diverse response: the population should not commit its activities along excessively narrow channels. Fourth is the principle of stability: the population should not change its mode of behavior every time the environment changes. Fifth is the 1946 principle of adaptability: the population must be able to change behavior mode when it’s worth the computational price.
- 15. Meta heuristic II Bee Colony Optimization Metaheuristic (BCO)
- 16. Meta heuristic II Agents (bees) collaborate in order to solve a difficult problem together Problems that can be applied: ● Traveling Salesman Problem (TSP) ● Quadratic Assignment Problem (QAP) ● Binary Knapsack Problem
- 17. Meta heuristic II Bees During the search process, artificial bees communicate directly. Each artificial bee makes a series of local moves, and in this way incrementally constructs a solution of the problem. Bees are adding solution components to the current partial solution until they create one or more feasible solutions. The search process is composed of iteration
- 18. Meta heuristic II Algorithms Pseudocode for the standard bees algorithm[2] 1 for i=1,…,ns i scout[i]=Initialise_scout() ii flower_patch[i]=Initialise_flower_patch(scout[i]) 2 do until stopping_condition=TRUE i Recruitment() ii for i =1,...,nb 1 flower_patch[i]=Local_search(flower_patch[i]) 2 flower_patch[i]=Site_abandonment(flower_patch[i]) 3 flower_patch[i]=Neighbourhood_shrinking(flower_patch[i]) iii for i = nb,...,ns 1 flower_patch[i]=Global_search(flower_patch[i])}
- 19. Meta heuristic II Traveling Salesman Problem
- 21. Meta heuristic III Ant Colony Optimization
- 22. Meta heuristic III Ant Colony Optimization - ACO It is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs.
- 23. Meta heuristic IV Cuckoo Search Cuckoos are fascinating birds, not only because of the beautiful sounds they make, but also because of their aggressive reproduction strategy. Some species named ani and Guira lay their eggs in communal nests, though they may remove others' eggs to increase the hatching probability of their own eggs. Quite a number of species engage in the mandatory brood parasitism by laying their eggs in the nests of other host birds (often other species). Cuckoo search (CS) is one of the latest nature-inspired metaheuristic algorithms, developed by Xin-She Yang and Suash Deb in 2009.
- 24. Comparative evaluation CS is based on the brood parasitism of some cuckoo species (Yang and Deb 2009). In addition, this algorithm is enhanced by the so- called Lévy flights, rather than by simple isotropic random walks (Pavlyukevich 2007). Recent studies show that CS is potentially far more efficient than PSO and genetic algorithms (Yang and Deb 2010).
- 25. Summary - GA and SA are not considered swarm methods - Thousands of algorithms with Swarm optimization methods - Each particle is a solution - It is not guaranteed to find the best solution
- 26. Conclusion - Metaheuristic vs heuristic - “Standard” metaheuristic - CS and recent developments - Several other techniques
- 27. References Blum and Roli, 2003 Yang and Deb 2009 Pavlyukevich 2007 Fred Glover in the 1970s James McCaffrey from Microsoft Research