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Ant Colony Optimization: an introduction
Daniel Chivilikhin
03.04.2013
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Outline
1. Biological inspiration of ACO
2. Solving NP-hard combinatorial problems
3. The ACO metaheuristic
4. ACO for the Traveling Salesman Problem
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Outline
1. Biological inspiration of ACO
2. Solving NP-hard combinatorial problems
3. The ACO metaheuristic
4. ACO for the Traveling Salesman Problem
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Biological inspiration:
from real to artificial ants
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Ant colonies
• Distributed systems of social insects
• Consist of simple individuals
• Colony intelligence >> Individual intelligence
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Ant Cooperation
• Stigmergy – indirect communication between individuals (ants)
• Driven by environment modifications
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Denebourg’s double bridge experiments
• Studied Argentine ants I. humilis
• Double bridge from ants to food source
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Double bridge experiments: equal lengths (1)
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Double bridge experiments: equal lengths (2)
• Run for a number of trials
• Ants choose each branch ~ same number of trials
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Double bridge experiments: different lengths (2)
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Double bridge experiments: different lengths (2)
• The majority of ants follow the short path
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Outline
1. Biological inspiration of ACO
2. Solving NP-hard combinatorial problems
3. The ACO metaheuristic
4. ACO for the Traveling Salesman Problem
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Solving NP-hard combinatorialproblems
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Combinatorial optimization
• Find values of discrete variables
• Optimizing a given objective function
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Combinatorial optimization
Π = (S, f, Ω) – problem instance
• S – set of candidate solutions
• f – objective function
• Ω – set of constraints
• – set of feasible solutions (with respect to Ω)
• Find globally optimal feasible solution s*
SS ~
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NP-hard combinatorial problems
• Cannot be exactly solved in polynomial time
• Approximate methods – generate near-optimal solutions in reasonable time
• No formal theoretical guarantees
• Approximate methods = heuristics
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Approximate methods
• Constructive algorithms
• Local search
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Constructive algorithms
• Add components to solution incrementally
• Example – greedy heuristics:
add solution component with best heuristic estimate
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Local search
• Explore neighborhoods of complete solutions
• Improve current solution by local changes– first improvement– best improvement
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What is a metaheuristic?
• A set of algorithmic concepts
• Can be used to define heuristic methods
• Applicable to a wide set of problems
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Examples of metaheuristics
• Simulated annealing
• Tabu search
• Iterated local search
• Evolutionary computation
• Ant colony optimization
• Particle swarm optimization
• etc.
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Outline
1. Biological inspiration of ACO
2. Solving NP-hard combinatorial problems
3. The ACO metaheuristic
4. ACO for the Traveling Salesman Problem
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The ACO metaheuristic
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ACO metaheuristic
• A colony of artificial ants cooperate in finding good solutions
• Each ant – simple agent
• Ants communicate indirectly using stigmergy
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Combinatorial optimization problem mapping (1)
• Combinatorial problem (S, f, Ω(t))
• Ω(t) – time-dependent constraints Example – dynamic problems
• Goal – find globally optimal feasible solution s*
• Minimization problem
• Mapped on another problem…
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Combinatorial optimization problem mapping (2)
• C = {c1, c2, …, cNc} – finite set of components
• States of the problem:
X = {x = <ci, cj, …, ch, …>, |x| < n < +∞}
• Set of candidate solutions: XS
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Combinatorial optimization problem mapping (3)
• Set of feasible states:
• We can complete into a solution satisfying Ω(t)
• Non-empty set of optimal solutions:
XX ~
Xx~
SXS ~*
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Combinatorial optimization problem mapping (4)
S* X~S
X• X – states
• S – candidate solutions
• – feasible states
• S* – optimal solutions
X~
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Combinatorial optimization problem mapping (5)
• Cost g(s, t) for each
• In most cases – g(s, t) ≡ f(s, t)
• GC = (C, L) – completely connected graph
• C – set of components
• L – edges fully connecting the components (connections)
• GC – construction graph
SsSs~
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Combinatorial optimization problem mapping (last )
• Artificial ants build solutions by performing randomized walks on GC(C, L)
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Construction graph
• Each component ci or connection lij have associated: heuristic information pheromone trail
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Heuristic information
• A priori information about the problem
• Does not depend on the ants
• On components ci – ηi
• On connections lij – ηij
• Meaning: cost of adding a component to the current
solution
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Pheromone trail
• Long-term memory about the entire search process
• On components ci – τi
• On connections lij – τij
• Updated by the ants
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Artificial ant (1)
• Stochastic constructive procedure
• Builds solutions by moving on GC
• Has finite memory for:– Implementing constraints Ω(t)– Evaluating solutions– Memorizing its path
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Artificial ant (2)
• Has a start state x
• Has termination conditions ek
• From state xr moves to a node from the neighborhood – Nk(xr)
• Stops if some ek are satisfied
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Artificial ant (3)
• Selects a move with a probabilistic rule depending on: Pheromone trails and heuristic information of
neighbor components and connections Memory Constraints Ω
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Artificial ant (4)
• Can update pheromone on visited components (nodes)
• and connections (edges)
• Ants act: Concurrently Independently
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The ACO metaheuristic
While not doStop():
ConstructAntSolutions()
UpdatePheromones()
DaemonActions()
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ConstructAntSolutions
• A colony of ants build a set of solutions
• Solutions are evaluated using the objective function
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UpdatePheromones
• Two opposite mechanisms: Pheromone deposit Pheromone evaporation
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UpdatePheromones: pheromone deposit
• Ants increase pheromone values on visited components and/or connections
• Increases probability to select visited components later
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UpdatePheromones: pheromone evaporation
• Decrease pheromone trails on all components/connections by a same value
• Forgetting – avoid rapid convergence to suboptimal solutions
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DaemonActions
• Optional centralized actions, e.g.: Local optimization Ant elitism (details later)
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ACO applications
• Traveling salesman• Quadratic assignment• Graph coloring• Multiple knapsack• Set covering• Maximum clique• Bin packing• …
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Outline
1. Biological inspiration of ACO
2. Solving NP-hard combinatorial problems
3. The ACO metaheuristic
4. ACO for the Traveling Salesman Problem
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ACO for the Traveling Salesman Problem
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Traveling salesman problem
• N – set of nodes (cities), |N| = n
• A – set of arcs, fully connecting N
• Weighted graph G = (N, A)
• Each arc has a weight dij – distance
• Problem:
Find minimum length Hamiltonian circuit
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TSP: construction graph
• Identical to the problem graph
• C = N
• L = A
• states = set of all possible partial tours
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TSP: constraints
• All cities have to be visited• Each city – only once• Enforcing – allow ants only to go to new
nodes
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TSP: pheromone trails
• Desirability of visiting city j directly after i
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TSP: heuristic information
• ηij = 1 / dij
• Used in most ACO for TSP
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TSP: solution construction
• Select random start city
• Add unvisited cities iteratively
• Until a complete tour is built
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ACO algorithms for TSP
• Ant System
• Elitist Ant System
• Rank-based Ant System
• Ant Colony System
• MAX-MIN Ant System
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Ant System: Pheromone initialization
• Pheromone initialization
Τij = m / Cnn,
where: m – number of ants Cnn – path length of nearest-neighbor
algorithm
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Ant System: Tour construction
• Ant k is located in city i
• is the neighborhood of city i
• Probability to go to city :
kiNl
ilil
ijijkijp
kiN
kiNj
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Tour construction: comprehension
• α = 0 – greedy algorithm
• β = 0 – only pheromone is at work quickly leads to stagnation
kiNl
ilil
ijijkijp
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Ant System: update pheromone trails – evaporation
Evaporation for all connections∀(i, j) ∈ L:
τij ← (1 – ρ) τij,
ρ ∈[0, 1] – evaporation rate
Prevents convergence to suboptimal solutions
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Ant System: update pheromone trails – deposit
• Tk – path of ant k• Ck – length of path Tk
• Ants deposit pheromone on visited arcs:
Ljim
k
kijijij
,,1
otherwise
TjiC kkkij
,0
,,/1
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Elitist Ant System
• Best-so-far ant deposits pheromone on each iteration:
bestij
m
k
kijijij e
1
otherwise
TjiC bestbestbestij
,0
,,/1
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Rank-based Ant System
• Rank all ants
• Each ant deposits amounts of pheromone proportional to its rank
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MAX-MIN Ant System
1. Only iteration-best or best-so-far ant deposits pheromone
2. Pheromone trails are limited to the interval [τmin, τmax]
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Ant Colony System
• Differs from Ant System in three points: More aggressive tour construction rule Only best ant evaporates and deposits
pheromone Local pheromone update
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Ant Colony System
otherwiseSystemAntlike
qqifj
ililNl ki
,
,maxarg 0
1.Tour Construction
2. Local pheromone update:
τij ← (1 – ξ)τij + ξτ0,
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Comparing Ant System variants
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State of the art in TSP
• CONCORDE http://www.tsp.gatech.edu/concorde.html
• Solved an instance of 85900 cities
• Computation took 286-2719 CPU days!
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Current ACO research activity
• New applications
• Theoretical proofs
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Further reading
• M. Dorigo, T. Stützle. Ant Colony Optimization. MIT Press, 2004.
• http://iridia.ulb.ac.be/~mdorigo/ACO/
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Next time…
• Some proofs of ACO convergence
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Thank you!
Any questions?
This presentation is available at:http://rain.ifmo.ru/~chivdan/presentations/
Daniel Chivilikhin [mailto: [email protected]]
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Used resources• http://teamaltman.com/wp-content/uploads
/2011/06/Uncertainty-Ant-Apple-1024x1024.jpg • http://myrealestatecoach.files.wordpress.com/2012/04/ant.jpg• http://ars.els-cdn.com/content/image/1-s2.0-S1568494613000264-gr3.jpg• http://www.theorie.physik.uni-goettingen.de/forschung/ha/talks/stuetzle.pdf • http://www.buyingandsellingwebsites.com/wp-content/uploads/2011/12/Ant-150.jpg• http://moodle2.gilbertschools.net/moodle/file.php/1040/Event-100_Days/Ant_Hormiga
.gif• http://4.bp.blogspot.com/_SPAe2p8Y-kg/TK3hQ8BUMtI/AAAAAAAAAHA/p75K-GcT_
oo/s1600/ant+vision.GIF• http://1.bp.blogspot.com/-tXKJQ4nSqOY/UQZ3vabiy_I/AAAAAAAAAjA
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