EE4E,M.Sc. C Programming

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EE4E,M.Sc. C Programming

• Assignment Introduction

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Assignment Introduction

• Aims/Objectives
• To produce a C implementation of the ANT colony
  optimisation algorithm (ACO)
• To apply to the travelling salesman problem

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ANT algorithms
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ANT algorithms

• Ants are practically blind but they still manage
  to find their way to and from food. How do they
  do it?
• These observations inspired a new type of
  algorithm called ant algorithms (or ant systems)
• These algorithms are very new (Dorigo, 1996) and
  is still very much a research area

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ANT algorithms

• Ant systems are a population based approach. In
  this respect it is similar to genetic algorithms
• There is a population of ants, with each ant
  finding a solution and then communicating with
  the other ants

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ANT algorithms

• Real ants can find the shortest path to a food
  source by laying a pheromone trail
• The ants which take the shortest path, lay the
  largest amount of pheromone per unit time
• Positive feedback reinforces this behaviour and
  more ants take the shortest path resulting in
  more pheromone being laid

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Travelling Salesman Problem

• Classic discrete optimisation problem
• Salesman needs to visit all cities just once and
  return back home

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Travelling Salesman Problem

• N cities -gt (N-1)! routes
• Currently TSPs involving 1000s cities are being
  studied
• You should restrict your algorithm to problems
  with lt100 cities!
• The ACO algorithm has proved to be an effective
  approach with performances close to optimal

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Travelling Salesman Problem

• Ants distribute an amount of pheremone on each
  part of its route in inverse proportion to the
  length of the route
• Doesnt quite mimic the behaviour of real ants!
• Typically the number of ants number of cities
• After every iteration t (all ants complete their
  routes), the pheremone trails are updated and new
  ants are generated

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Travelling Salesman Problem

• In practice, applying ACO to TSP is a compromise
  between reinforcing previous ant behaviour and
  exploring new solutions
• Probabilistic decisions
• Pheromone evaporation

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Implementation

• Use any convenient programming platform
• Think about presentation of results and user
  interfaces
• Console based I/O ok but GUIs more flexible and
  user friendly
• Important to separate out ANT-based classes and
  the problem domain (TSP) classes so that either
  can be used separately

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Assessment

• Programming report (deadlines are on the handout)
• Follow closely the marking pro-forma
• The report should contain discussions about
  object orientation, code re-useability, object
  interaction, algorithm performance and
  comparisons (close to optimal?)
• A formal design discussion is not expected but
  informal class/object diagrams and pseudo-code
  should be used

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Assessment

• Include with your report your software with
  enough information to allow me to run it
• Maybe include a user guide
• I will base the marks partly on the observed
  functionality