This site uses cookies to deliver our services and to show you relevant ads and presentations. By clicking on "Accept", you acknowledge that you have read and understand our Cookie Policy , Privacy Policy , and our Terms of Use.

Download A Ant Colony Optimization (ACO) PowerPoint Presentation

Login   OR  Register

Iframe embed code :

Presentation url :


Description :

PowerPoint presentation on A Ant Colony Optimization (ACO), download now ppt of A Ant Colony Optimization (ACO)

Tags :

A Ant Colony Optimization (ACO)

Home / Forest & Animals / Forest & Animals Presentations / A Ant Colony Optimization (ACO) PowerPoint Presentation

A Ant Colony Optimization (ACO) PowerPoint Presentation

Ppt Presentation Embed Code   Zoom Ppt Presentation

About This Presentation

Description : PowerPoint presentation on A Ant Colony Optimization (ACO), download now ppt of A Ant Colony Optimiz... Read More

Tags : A Ant Colony Optimization (ACO)

Published on : Jan 08, 2015
Views : 851 | Downloads : 0

Download Now

Share on Social Media


PowerPoint is the world's most popular presentation software which can let you create professional A Ant Colony Optimization (ACO) powerpoint presentation easily and in no time. This helps you give your presentation on A Ant Colony Optimization (ACO) in a conference, a school lecture, a business proposal, in a webinar and business and professional representations.

The uploader spent his/her valuable time to create this A Ant Colony Optimization (ACO) powerpoint presentation slides, to share his/her useful content with the world. This ppt presentation uploaded by worldwideweb in Forest & Animals ppt presentation category is available for free download,and can be used according to your industries like finance, marketing, education, health and many more. provides a platform to marketers, presenters and educationists along with being the preferred search engine for professional PowerPoint presentations on the Internet to upload their A Ant Colony Optimization (ACO) ppt presentation slides to help them BUILD THEIR CROWD!!

Slide 1 - Ant Colony Optimization (ACO): Applications to Scheduling Franco Villongco IEOR 4405 4/28/09
Slide 2 - Definition Metaheuristic: similar to genetic algorithms, simulated annealing etc. Flexible enough to be applied to combinatorial optimization problems.
Slide 3 - Inspiration Foraging behavior of real ants Blind ants communicate through stigmergy Leave pheromone trails to make a certain path more likely to be traversed by other ants
Slide 4 - Two-bridge Experiment NEST FOOD
Slide 5 - Two-bridge Experiment NEST FOOD
Slide 6 - Problem Representation (S, f, Ω) S: set of candidate solution f: objective function of s є S Ω: set of constraints Set C ={ c1, c2… cN} where N is the number of components Problem states are defined as x = ( ci, cj… ch) We call χ the set of all states
Slide 7 - Problem Representation Nonempty set S* of optimal solutions GC = (C,L) whose nodes are the components. Artificial ants then build solutions by performing walks on the complete graph Like in the two-bridge experiment, arcs (trails) that have more pheromone will have a higher probability of being chosen.
Slide 8 - Scheduling Applications Jm||Cmax We use Ant System algorithm GC = (C,L) consists of all the operations and two additional nodes for a source and sink node. Our constraints Ω are simply the precedence constraints.
Slide 9 - Scheduling Applications Pheromone trail τij on the arc (i,j) indicates the desirability of choosing operation j directly after operation i. heuristic information associated with that operation ηj
Slide 10 - Scheduling Applications At each iteration of the construction procedure, m ants concurrently build solutions After each iteration, pheromone evaporation will be applied on all arcs: Where the parameter ρ є (0,1)
Slide 11 - Scheduling Applications The better Cmax is for the solution constructed by a particular ant k, the more pheromone there will be to the arcs corresponding to that solution:
Slide 12 - Scheduling Applications Any ant at node i will choose node j with probability Where Nk is the set of feasible operations nj is the heuristic value proportional to the amount of work remaining corresponding to the job of the operation considered
Slide 13 - Scheduling Applications 1||ΣTjwj We use the Ant Colony System algorithm Same as AS but with differences in pheromone updates and ant decision rule For our construction graph, we have for our node the n positions and n jobs Pheromone trail τij indicate the desirability of scheduling job j to position i heuristic information ηj inversely proportional to job j’s deadline
Slide 14 - Scheduling Applications Main differences Pheromone update (global): Only the best-so-far solution increases in pheromone For all (i,j) in sbs (best-so-far solution) and where
Slide 15 - Scheduling Applications Pheromone update (local): applied during the iteration to the arcs (i,j) that were traversed
Slide 16 - Scheduling Applications Now, in choosing the next job j to schedule the probability of choosing job j is Where J is the random variable that will equal j with probability
Slide 17 - Thank You!