Objective Introduce principle of Ant System(AS) Inspiration for
your research Algorithm term project
Slide 4
Agenda Inspiration Biological Background Adapt to Computer
System Ant System Travel Salesman Problem Model Definition Ant
Cycle Algorithm Parameter Setting Basic Parameter setting from
Model definition Synergistic Effects
Slide 5
Agenda(contd) Properties Initialization Elitist Strategy
Problem Size Experiment Travel Salesman Problem Asymmetric Travel
Salesman Problem Conclusions and Extensions Contribution of this
Paper Extension
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Inspiration Biological Background Adapt to Computer System
Slide 7
Biological background One of the problems studied by
ethologists was to understand how almost blind animals like ants
could manage to establish shortest route paths from their colony to
feeding sources and back The Answer is Pheromone Trails
Slide 8
What is Pheromone(from wiki) A pheromone (from Greek phero "to
bear" + hormone") is a chemical that triggers a natural behavioral
response in another member of the same species. There are alarm
pheromones, food trail pheromones, sex pheromones, and many others
that affect behavior or physiology. Their use among insects has
been particularly well documented, although many vertebrates and
plants also communicate using pheromones.
Slide 9
Trail Pheromones(from wiki) Trail pheromones are common in
social insects. For example, ants mark their paths with these
pheromones, which are non-volatile hydrocarbons. Certain ants lay
down an initial trail of pheromones as they return to the nest with
food. This trail attracts other ants and serves as a guide. As long
as the food source remains, the pheromone trail will be continually
renewed. The pheromone must be continually renewed because it
evaporates quickly. When the supply begins to dwindle, the
trailmaking ceases. In at least one species of ant, trails that no
longer lead to food are also marked with a repellent
pheromone.
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Biological background(contd)
Slide 11
Adapt to computer system Artificial Ants will have some Memory
They will not be completely Blind They will live an environment
where time is discrete
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Adapt to computer system(contd)
Slide 13
Ant System TSP problem (AS is designed to solve TSP Problem
originally) Model Definition Ant Cycle Algorithm
Slide 14
Travel Salesman Problem Given a graph G=(V,E), where: V:set of
Cities E:set of edges between Cities and :Euclidean distance
between i and j The problem is finding a minimal length closed tour
that visits each town once
Slide 15
Travel Salesman Problem(contd) TSP NPC, you can reduce HC to
NPC For this problem, each simple agent have the following
characteristics: (1)it choose the town to go with a function of the
town distance and amount of trail (Pheromone Trails) (2)used a tabu
list to make a legal tours (3)lays a substance called trail on each
edge(i,j) visited, when it completes a tour (4)Each agent at time t
chooses the next town, where it will be at time t+1
Slide 16
Model Definition iteration and cycle tabu list Trail intensity
update formula Transition probability formula(decide where to
go)
Slide 17
Iteration and Cycle (t)(i=1,,n) be the number of ants in town i
at time t m=, total number of ants an iteration of the AS algorithm
the m moves carried out by the m ants in the interval (t,t+1), and
every n iteration of the algorithm called a cycle(completed a
tour)
Slide 18
Tabu list associate with each ant a data structure called the
tabu list, that saves the town already visited and forbids the ant
to visit them again before a cycle completed. when a cycle is
completed, the tabu list is used to compute the ants travel
distance
Slide 19
Tabu Search(from wiki) Tabu search is a mathematical
optimization method, belonging to the class of local search
techniques. Tabu search enhances the performance of a local search
method by using memory structures: once a potential solution has
been determined, it is marked as "taboo" ("tabu" being a different
spelling of the same word) so that the algorithm does not visit
that possibility repeatedly. Tabu search is attributed to Fred
Glover.
Slide 20
Trail intensity update formula :intensity of trail on edge
(i,j) at time t Trail intensity update formula: : is a coefficient
such that(1 - )represents the evaporation of trail between time t
and t+n(