51667267 ant colony optimization ppt

8/13/2019 51667267 Ant Colony Optimization PPT

1/13

Ant colony optimization (ACO)

Presented by

Don Baechtel


2/13

Ant colony optimization algorithm

A probabilistictechnique for solving computationalproblems which can be reduced to finding good pathsthrough graphs.

Is a member of ant colony algorithmsfamily, in swarmintelligencemethods, and it constitutes somemetaheuristicoptimizations.

Aims to search for an optimal path in a graph, based onthe behavior of antsseeking a path between their colonyand a source of food.

Has since diversified to solve a wider class of numericalproblems, and as a result, several problems have emerged,drawing on various aspects of the behavior of ants.
http://en.wikipedia.org/wiki/Probabilityhttp://en.wikipedia.org/wiki/Graph_(mathematics)http://en.wikipedia.org/wiki/Swarm_intelligencehttp://en.wikipedia.org/wiki/Swarm_intelligencehttp://en.wikipedia.org/wiki/Metaheuristichttp://en.wikipedia.org/wiki/Antshttp://en.wikipedia.org/wiki/Ant_colonyhttp://en.wikipedia.org/wiki/Ant_colonyhttp://en.wikipedia.org/wiki/Antshttp://en.wikipedia.org/wiki/Metaheuristichttp://en.wikipedia.org/wiki/Swarm_intelligencehttp://en.wikipedia.org/wiki/Swarm_intelligencehttp://en.wikipedia.org/wiki/Graph_(mathematics)http://en.wikipedia.org/wiki/Probability


3/13

Ant colony behavior In the natural world, ants (initially) wander randomly, and upon

finding foodreturn to their colony while laying downpheromonetrails.

If other ants find such a path, they are likely not to keeptravelling at random, but to instead follow the trail, returningand reinforcing it if they eventually find food.

Over time the pheromone trail starts to evaporate, thusreducing its attractive strength.

The more time it takes for an ant to travel down the path andback again, the more time the pheromones have to evaporate.

A short path, by comparison, gets marched over faster, andthus the pheromone density remains high as it is laid on thepath as fast as it can evaporate.

Pheromone evaporation has the advantage of avoiding theconvergence to a locally optimal solution.
http://en.wikipedia.org/wiki/Randomhttp://en.wikipedia.org/wiki/Pheromonehttp://en.wikipedia.org/wiki/Pheromonehttp://en.wikipedia.org/wiki/Pheromonehttp://en.wikipedia.org/wiki/Random


4/13

Ant colony behavior


5/13

Ant colony behavior

The original idea comes from observing the exploitation of foodresources among ants, in which ants individually limited cognitiveabilities have collectively been able to find the shortest pathbetween a food source and the nest.

The first ant finds the food source (F), via any way (a), then returns

to the nest (N), leaving behind a trail pheromone (b) Ants indiscriminately follow four possible ways, but the

strengthening of the runway makes it more attractive as theshortest route.

Ants take the shortest route, long portions of other ways lose theirtrail pheromones.

In a series of experiments on a colony of ants with a choicebetween two unequal length paths leading to a source of food,biologists have observed that ants tended to use the shortest route.


6/13

Modeling Ant colony behavior

An ant runs more or less at random around the colony. If it discovers a food source, it returns more or less directly to

the nest, leaving in its path a trail of pheromone.

These pheromones are attractive, nearby ants will be inclinedto follow, more or less directly, the track.

Returning to the colony, these ants will strengthen the route. If there are two routes to reach the same food source then, in

a given amount of time, the shorter one will be traveled bymore ants than the long route.

The short route will be increasingly enhanced, and thereforebecome more attractive.

The long route will eventually disappear because pheromonesare volatile.

Eventually, all the ants have determined and therefore"chosen" the shortest route.


7/13

Use of Environment for Communication

Ants use the environment as a medium ofcommunication.

They exchange information indirectly by depositingpheromones, all detailing the status of their "work".

The information exchanged has a local scope, only anant located where the pheromones were left has anotion of them.

This system is called "Stigmergy" and occurs in manysocial animal societies (it has been studied in the caseof the construction of pillars in the nests of termites).
http://en.wikipedia.org/wiki/Stigmergyhttp://en.wikipedia.org/wiki/Stigmergy


8/13

Stigmergy

A mechanism of indirect coordinationbetween agents oractions.

The principle is that the trace left in the environmentby anaction stimulates the performance of a next action, by thesame or a different agent.

In that way, subsequent actions tend to reinforce and build oneach other, leading to the spontaneous emergence ofcoherent, apparently systematic activity.

Stigmergy is a form of self-organization. It produces complex,seemingly intelligent structures, without need for any

planning, control, or even direct communication between theagents.

As such it supports efficient collaboration between extremelysimple agents, who lack any memory, intelligence or evenawareness of each other.
http://en.wikipedia.org/wiki/Coordinationhttp://en.wikipedia.org/wiki/Natural_environmenthttp://en.wikipedia.org/wiki/Self-organizationhttp://en.wikipedia.org/wiki/Self-organizationhttp://en.wikipedia.org/wiki/Self-organizationhttp://en.wikipedia.org/wiki/Self-organizationhttp://en.wikipedia.org/wiki/Natural_environmenthttp://en.wikipedia.org/wiki/Coordination


9/13


The mechanism to solve a problem too complex to beaddressed by single ants is a good example of a self-organizedsystem.

This system is based on positive feedback (the deposit ofpheromone attracts other ants that will strengthen it

themselves) and negative (dissipation of the route byevaporation prevents the system from thrashing).

Theoretically, if the quantity of pheromone remained the sameover time on all edges, no route would be chosen.

However, because of feedback, a slight variation on an edgewill be amplified and thus allow the choice of an edge.

The algorithm will move from an unstable state in which noedge is stronger than another, to a stable state where the routeis composed of the strongest edges.


10/13


The basic philosophy of the algorithm involves the movement of acolony of ants through the different states of the problem influencedby two local decision policies, viz., trailsand attractiveness.

Thereby, each such ant incrementally constructs a solution to theproblem.

When an ant completes a solution, or during the construction phase,the ant evaluates the solution and modifies the trail value on thecomponents used in its solution.

This pheromone information will direct the search of the future ants.

Furthermore, the algorithm also includes two more mechanisms, viz.,trailevaporationand daemonactions.

Trailevaporationreduces all trail values over time thereby avoiding anypossibilities of getting stuck in local optima.

The daemonactionsare used to bias the search process from a non-local perspective.


11/13



12/13

ApplicationsScheduling problems Job-shop scheduling problem (JSP)[10]

Open-shop scheduling problem (OSP)[11][12]

Permutation flow shop problem (PFSP)[13]

Single machine total tardiness problem (SMTTP)[14]

Single machine total weighted tardiness problem(SMTWTP)[15][16][17]

Resource-constrained project scheduling problem(RCPSP)[18]

Group-shop scheduling problem (GSP)[19]

Single-machine total tardiness problem with sequencedependent setup times (SMTTPDST)[20]

Multistage Flowshop Scheduling Problem (MFSP) withsequence dependent setup/changeover times[21]

Vehicle routing problems Capacitated vehicle routing problem (CVRP)[22][23][24]

Multi-depot vehicle routing problem (MDVRP)[25]

Period vehicle routing problem (PVRP)[26] Split delivery vehicle routing problem (SDVRP)[27]

Stochastic vehicle routing problem (SVRP)[28]

Vehicle routing problem with pick-up and delivery(VRPPD)[29][30]

Vehicle routing problem with time windows(VRPTW)[31][32][33]

Time Dependent Vehicle Routing Problem with TimeWindows (TDVRPTW)[34]

Assignment problems Quadratic assignment problem (QAP)[35]

Generalized assignment problem (GAP)

[36][37]

Frequency assignment problem (FAP)[38]

Redundancy allocation problem (RAP)[39]

Set problems Set covering problem(SCP)[40][41]

Set partition problem (SPP)[42]

Weight constrained graph tree partition problem(WCGTPP)[43]

Arc-weighted l-cardinality tree problem (AWlCTP)[44] Multiple knapsack problem (MKP)[45]

Maximum independent set problem (MIS)[46]

Others Classification[47]

Connection-oriented network routing[48]

Connectionless network routing[49][50]

Data mining[47][51][52]

Discounted cash flows in project scheduling[53]

Distributed Information Retrieval[54][55]

Grid Workflow Scheduling Problem[56]

Image processing[57][58]

Intelligent testing system[59]

System identification[60][61]

Protein Folding[62][63]

Power Electronic Circuit Design[64]


13/13

Multi-Ant Colony Algorithm

Allows for the simultaneous exploration of several searchlocations.

And to dynamically intensify the search on the mostpromising ones.

Each colony maintains its own trail and set of parameters.

The most successful colonies transfer information to theworst ones.

Specifically the trails of the worst colonies are periodically

updated, which hopefully will help them to escape fromlocal optima and move towards more promising locations.

Includes a mutation operator that aims to adjust theparameter settings during the optimization.

51667267 ant colony optimization ppt

Documents