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EE4E,M.Sc. C++ Programming

EE4E,M.Sc. C++ Programming. Assignment Introduction. Assignment Introduction. Aims/Objectives To produce a C++ implementation of the ANT colony optimisation algorithm (ACO) To apply to the travelling salesman problem. ANT algorithms. ANT algorithms.

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EE4E,M.Sc. C++ Programming

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  1. EE4E,M.Sc. C++ Programming Assignment Introduction

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

  3. ANT algorithms

  4. 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

  5. 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

  6. 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

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

  8. Travelling Salesman Problem • N cities -> (N-1)! routes • Currently TSP’s involving 1000’s cities are being studied • You should restrict your algorithm to problems with <100 cities! • The ACO algorithm has proved to be an effective approach with performances close to optimal

  9. Travelling Salesman Problem • Ants distribute an amount of pheremone on each part of its route in inverse proportion to the length of the route • Doesn’t 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

  10. 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

  11. Implementation • Use any convenient programming platform • Think about presentation of results and user interfaces • Console based I/O ok but GUI’s 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

  12. 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

  13. 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

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