Cover of Swarm Intelligence Book
Andries P Engelbrecht Fundamentals of Computational Swarm Intelligence

Ant Colony Optimisation Algorithms

The following classes are available (all discussions assume CIlib version 0.5, and therefor assumes the same directory structure as used in CIlib):

  • Ant Colony Optimisation [ Hide ]

    Find below the XML specification for a standard Ant Colony Optimisation (section 25.1) algorithm solving the Traveling Salesman Problem.

    <simulator>
      <algorithms>
        <algorithmid="astsp"class="aco.ASTSP"numberAnts="6"tau="0.000001">
          <prototypeAntclass="aco.TSPAnt">
            <transitionRuleFunctionclass="aco.StandardTransitionRuleFunction"alpha="1.0"beta="5.0"/>
            <pheromoneUpdateclass="aco.pheromone.StandardPheromoneUpdate"rho="0.5"e="5.0"Q="100.0"/>
          </prototypeAnt>
          <addStoppingConditionclass="stoppingcondition.MaximumIterations"iterations="1000"/>
        </algorithm>
      </algorithms>
      <problems>
        <problemid="TSP"class="aco.TSPProblem">
          <dataSetBuilderclass="problem.dataset.StringDataSetBuilder">
            <dataSetclass="problem.dataset.LocalDataSet"file="data/pr107.tsp"/>
          </dataSetBuilder>
        </problem>
      </problems>
      <measurementsid="measurements"class="simulator.MeasurementSuite"samples="2"resolution="1">
        <addMeasurementclass="aco.GraphMeasurementSolutionLength"/>
        <addMeasurementclass="aco.GraphMeasurementSolution"/>
      </measurements>
      <simulations>
        <simulation>
          <algorithmidref="astsp"/>
          <problemidref="TSP"/>
          <measurementsidref="measurements"file="data/results-aco-astsp.txt"/>
        </simulation>
      </simulations>
    </simulator>

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