Ant Colony Optimizationir.linkedin.com/in/omidedriss

github.com/omidedrissOmid Edriss

Components of Soft Computing

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Particle swarm optimization

Ant colony optimization

Artificial bee colony algorithm

Grey wolf optimizer

Bacterial colony optimization

Soft

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ANN

Evolutionary Computation

Fuzzy Logic

Components of swarm Intelligence

Population

collaboration

communicationExchange of information

self-organize

Information stream

applications

• Scheduling problem

• Job-shop scheduling problem (JSP)

• Open-shop scheduling problem (OSP)

• Permutation flow shop problem (PFSP)

• Single machine total tardiness problem (SMTTP)

• Single machine total weighted tardiness problem (SMTWTP)

• Resource-constrained project scheduling problem (RCPSP)

• Group-shop scheduling problem (GSP)

• Single-machine total tardiness problem with sequence dependent setup times (SMTTPDST)

• Multistage Flowshop Scheduling Problem (MFSP) with sequence dependent setup/changeover times

applications

• Vehicle routing problem

• Capacitated vehicle routing problem (CVRP)

• Multi-depot vehicle routing problem (MDVRP)

• Period vehicle routing problem (PVRP)

• Split delivery vehicle routing problem (SDVRP)

• Stochastic vehicle routing problem (SVRP)

• Vehicle routing problem with pick-up and delivery (VRPPD)

• Vehicle routing problem with time windows (VRPTW)

• Time Dependent Vehicle Routing Problem with Time Windows (TDVRPTW)

• Vehicle Routing Problem with Time Windows and Multiple Service Workers (VRPTWMS)

applications

• Assignment problem

• Set problem

• Device Sizing Problem in Nanoelectronics Physical Design

• Image Processing

• ACO algorithm is used in image processing for image edge detection and edge linking

Naturally Observed Ant Behavior

Ant Colony Optimization (ACO)

All is well in the world of the ant.

Naturally Observed Ant Behavior

Ant Colony Optimization (ACO)

Oh no! An obstacle has blocked our path!

Naturally Observed Ant Behavior

Ant Colony Optimization (ACO)

?

Naturally Observed Ant Behavior

Ant Colony Optimization (ACO)

Shorter path reinforced

!

Naturally Observed Ant Behavior

Naturally Observed Ant Behavior

Naturally Observed Ant Behavior

Naturally Observed Ant Behavior

Naturally Observed Ant Behavior

Naturally Observed Ant Behavior

Naturally Observed Ant Behavior

Travelling Salesman Problem (TSP)

Distribute ants

TSP

Mathematical concepts

Mathematical concepts

Mathematical concepts

Difference step ACO

• 1) Preparations• 2) For each of Ants these steps :

• A) Select a random point• B) Complete these steps to do

• B-1) Choose one of the selectable cities• B-2) We will delete the selected city

• C) After construction of the way, we evaluate• 3) After all the ants in their ways

• A) Update Pheromones• B) Evaporation

• 4) Go to Step 2

Types of end of the algorithm

Software engineering Optimization algorithm

• Create Model

• Cost Function

• NVAR

Problem Definition

• Parameter of end of the algorithm

• Parameters of problem

Parameters• Create and

Initialization Population

• Parameters of problem

Initialization

• Kernel algorithm

• Move Ants, Update Phromones , Evaporation

Main Loop• Display and get

solution

Display & Result

Reference

• Dorigo, Marco and Stützle, Thomas. (2004) Ant Colony Optimization,Cambridge, MA: The MIT Press.

• Dorigo, Marco, Gambardella, Luca M., Middendorf, Martin. (2002) “Guest Editorial,” IEEE Transactions on Evolutionary Computation, 6(4): 317-320.

• Ant Colony Optimization. A metaheuristic approach to hard network optimization problems , university of central Florida

• Presentation ACO dr.K.haris

Questions, Comments?

Thank You