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Add standard param files that define Ant System and Ant Colony System…
… algorithms. Revised the knapsack and TSP examples to use these, making for pretty simple examples of using ACO.
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@@ -11,4 +11,5 @@ ecj/src/main/resources/ec/app/mona/1-MonaLisaOut.png | |
| *.idx | ||
| *.log | ||
| *.out | ||
| *.toc | ||
| *.toc | ||
| *~ | ||
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| /* | ||
| Copyright 2019 by Sean Luke | ||
| Licensed under the Academic Free License version 3.0 | ||
| See the file "LICENSE" for more information | ||
| */ | ||
| package ec.co.ant; | ||
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| import ec.EvolutionState; | ||
| import ec.Individual; | ||
| import ec.Subpopulation; | ||
| import ec.co.Component; | ||
| import ec.co.ConstructiveIndividual; | ||
| import ec.util.Parameter; | ||
| import java.util.Arrays; | ||
| import java.util.List; | ||
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| /** | ||
| * | ||
| * @author Eric O. Scott | ||
| */ | ||
| public class ACSLocalUpdateRule implements UpdateRule { | ||
| final public static String P_RHO = "rho"; | ||
| final public static String P_MINIMUM_PHEROMONE = "minimum-pheromone"; | ||
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| private double rho; | ||
| private double minimumPheromone; | ||
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| public double getMinimumPheromone() { return minimumPheromone; } | ||
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| public double getRho() { return rho; } | ||
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| @Override | ||
| public void setup(final EvolutionState state, final Parameter base) | ||
| { | ||
| assert(state != null); | ||
| assert(base != null); | ||
| rho = state.parameters.getDouble(base.push(P_RHO), null, 0.0); | ||
| minimumPheromone = state.parameters.getDouble(base.push(P_RHO), null, 0.0); | ||
| assert(repOK()); | ||
| } | ||
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| @Override | ||
| public void updatePheromones(final EvolutionState state, final PheromoneTable pheromones, final List individuals) { | ||
| assert(pheromones != null); | ||
| assert(individuals != null); | ||
| assert(!individuals.isEmpty()); | ||
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| for (final Object ind : individuals) | ||
| { | ||
| for (final Object oo : (ConstructiveIndividual)ind) | ||
| { | ||
| assert(oo instanceof Component); | ||
| final Component c = (Component) oo; | ||
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| final double oldPheromone = pheromones.get(state, c, 0); // Using thread 0 because we are in a single-threaded function | ||
| pheromones.set(c, (1-rho)*oldPheromone + rho*minimumPheromone); | ||
| } | ||
| } | ||
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| assert(repOK()); | ||
| } | ||
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| public final boolean repOK() | ||
| { | ||
| return P_RHO != null | ||
| && !P_RHO.isEmpty() | ||
| && !Double.isInfinite(rho) | ||
| && !Double.isNaN(rho) | ||
| && rho >= 0.0; | ||
| } | ||
| } |
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| # Copyright 2019 by Sean Luke and George Mason University | ||
| # Licensed under the Academic Free License version 3.0 | ||
| # See the file "LICENSE" for more information | ||
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| ############################## | ||
| # Ant Colony System applied to TSP | ||
| ############################## | ||
| parent.0 = ../../co/ant/acs.params | ||
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| generations = 200 | ||
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| # Population size sets the number of ants per generation | ||
| pop.subpop.0.size = 20 | ||
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| ############################## | ||
| # AntSpecies holds the construction and update rules that define ACO algorithms | ||
| ############################## | ||
| pop.subpop.0.species.ind = ec.co.ConstructiveIndividual | ||
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| # AS uses SimpleConstructionRule to implement psuedo-random proportionate selection | ||
| pop.subpop.0.species.construction-rule = ec.co.ant.SimpleConstructionRule | ||
| pop.subpop.0.species.construction-rule.component-selector = ec.co.ant.PseudorandomProportionateComponentSelector | ||
| pop.subpop.0.species.construction-rule.component-selector.prob-best = 0.5 | ||
| pop.subpop.0.species.construction-rule.component-selector.alpha = 1.0 | ||
| pop.subpop.0.species.construction-rule.component-selector.beta = 1.0 | ||
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| # Here we specify ACS's global rule for pheromone updates, using the global best ant | ||
| # to deposite pheromones at the generation boundary | ||
| pop.subpop.0.species.update-rule = ec.co.ant.GlobalUpdateRule | ||
| pop.subpop.0.species.update-rule.rho = 0.3 | ||
| # You could switch this to ITERATION-BEST if you like | ||
| pop.subpop.0.species.update-rule.best-strategy = GLOBAL_BEST | ||
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| # Here we add ACS's local update rule, which decayse pheromones visited by each ant as soon as it is evaluated (rather than waiting for the generation boundary) | ||
| pop.subpop.0.species.local-update-rule = ec.co.ant.ACSLocalUpdateRule | ||
| pop.subpop.0.species.local-update-rule.rho = 0.1 | ||
| pop.subpop.0.species.local-update-rule.minimum-pheromone = 0.000001 | ||
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| ############################## | ||
| # Problem | ||
| ############################## | ||
| eval.problem = ec.app.knapsack.KnapsackProblem | ||
| # Example instance P01 from http://people.sc.fsu.edu/~jburkardt/datasets/knapsack_01/knapsack_01.html | ||
| eval.problem.knapsack-size = 165 | ||
| eval.problem.sizes = 23 31 29 44 53 38 63 85 89 82 | ||
| eval.problem.values = 92 57 49 68 60 43 67 84 87 72 |
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| @@ -0,0 +1,46 @@ | ||
| # Copyright 2019 by Sean Luke and George Mason University | ||
| # Licensed under the Academic Free License version 3.0 | ||
| # See the file "LICENSE" for more information | ||
|
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| ############################## | ||
| # Ant Colony System applied to TSP | ||
| ############################## | ||
| parent.0 = ../../co/ant/acs.params | ||
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| generations = 200 | ||
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| # Population size sets the number of ants per generation | ||
| pop.subpop.0.size = 20 | ||
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| ############################## | ||
| # AntSpecies holds the construction and update rules that define ACO algorithms | ||
| ############################## | ||
| pop.subpop.0.species.ind = ec.app.tsp.TSPIndividual | ||
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| # AS uses SimpleConstructionRule to implement psuedo-random proportionate selection | ||
| pop.subpop.0.species.construction-rule = ec.co.ant.SimpleConstructionRule | ||
| pop.subpop.0.species.construction-rule.component-selector = ec.co.ant.PseudorandomProportionateComponentSelector | ||
| pop.subpop.0.species.construction-rule.component-selector.prob-best = 0.5 | ||
| pop.subpop.0.species.construction-rule.component-selector.alpha = 1.0 | ||
| pop.subpop.0.species.construction-rule.component-selector.beta = 1.0 | ||
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| # Here we specify ACS's global rule for pheromone updates, using the global best ant | ||
| # to deposite pheromones at the generation boundary | ||
| pop.subpop.0.species.update-rule = ec.co.ant.GlobalUpdateRule | ||
| pop.subpop.0.species.update-rule.rho = 0.3 | ||
| # You could switch this to ITERATION-BEST if you like | ||
| pop.subpop.0.species.update-rule.best-strategy = GLOBAL_BEST | ||
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| # Here we add ACS's local update rule, which decayse pheromones visited by each ant as soon as it is evaluated (rather than waiting for the generation boundary) | ||
| pop.subpop.0.species.local-update-rule = ec.co.ant.ACSLocalUpdateRule | ||
| pop.subpop.0.species.local-update-rule.rho = 0.1 | ||
| pop.subpop.0.species.local-update-rule.minimum-pheromone = 0.000001 | ||
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| ############################## | ||
| # Problem | ||
| ############################## | ||
| eval.problem = ec.app.tsp.TSPProblem | ||
| #eval.problem.file = $src/main/resources/ec/app/tsp/test4.tsp | ||
| eval.problem.file = $src/main/resources/ec/app/tsp/berlin52.tsp |
22 changes: 11 additions & 11 deletions
22
...in/resources/ec/app/tsp/aco.master.params → ...ain/resources/ec/app/tsp/as.master.params
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| @@ -1,11 +1,11 @@ | ||
| # Copyright 2018 by Sean Luke and George Mason University | ||
| # Licensed under the Academic Free License version 3.0 | ||
| # See the file "LICENSE" for more information | ||
| parent.0 = aco.params | ||
| parent.1 = ../../eval/master.params | ||
| # That's it! We're not changing any of the parameters | ||
| # but you might want to look at your options in | ||
| # ec/eval/master.params | ||
| # Copyright 2018 by Sean Luke and George Mason University | ||
| # Licensed under the Academic Free License version 3.0 | ||
| # See the file "LICENSE" for more information | ||
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| parent.0 = as.params | ||
| parent.1 = ../../eval/master.params | ||
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| # That's it! We're not changing any of the parameters | ||
| # but you might want to look at your options in | ||
| # ec/eval/master.params | ||
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66 changes: 33 additions & 33 deletions
66
...ain/resources/ec/app/tsp/aco.slave.params → ...main/resources/ec/app/tsp/as.slave.params
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| # Copyright 2006 by Sean Luke and George Mason University | ||
| # Licensed under the Academic Free License version 3.0 | ||
| # See the file "LICENSE" for more information | ||
| parent.0 = aco.master.params | ||
| # You may want to customize each slave by giving it a unique | ||
| # name like this: | ||
| # | ||
| # eval.slave.name = my-slave-name | ||
| # | ||
| # (do this differently for each slave:, slave-2, slave-3, etc.) | ||
| # Otherwise, each slave will give itself an approximately unique name based | ||
| # on the current time and the IP address of the slave. The unique name | ||
| # doesn't HAVE to be unique -- it's just for printing/debug purposes. | ||
| # We want to make sure that the Slave's Statistics object does not | ||
| # override the Master's statistics files. The easiest way to do this | ||
| # is to eliminate the Statistics object in the slave by setting it | ||
| # to the (empty default) Statistics class with no children. | ||
| eval.stat = ec.Statistics | ||
| eval.stat.num-children = 0 | ||
| # The slave needs to know where the Master is. You need to | ||
| # change this. | ||
| eval.master.host = 127.0.0.1 | ||
| eval.return-inds = false | ||
| evalthreads = 1 | ||
| # Copyright 2006 by Sean Luke and George Mason University | ||
| # Licensed under the Academic Free License version 3.0 | ||
| # See the file "LICENSE" for more information | ||
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| parent.0 = as.master.params | ||
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| # You may want to customize each slave by giving it a unique | ||
| # name like this: | ||
| # | ||
| # eval.slave.name = my-slave-name | ||
| # | ||
| # (do this differently for each slave:, slave-2, slave-3, etc.) | ||
| # Otherwise, each slave will give itself an approximately unique name based | ||
| # on the current time and the IP address of the slave. The unique name | ||
| # doesn't HAVE to be unique -- it's just for printing/debug purposes. | ||
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| # We want to make sure that the Slave's Statistics object does not | ||
| # override the Master's statistics files. The easiest way to do this | ||
| # is to eliminate the Statistics object in the slave by setting it | ||
| # to the (empty default) Statistics class with no children. | ||
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| eval.stat = ec.Statistics | ||
| eval.stat.num-children = 0 | ||
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| # The slave needs to know where the Master is. You need to | ||
| # change this. | ||
| eval.master.host = 127.0.0.1 | ||
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| eval.return-inds = false | ||
| evalthreads = 1 | ||
| eval.slave.one-shot = false |
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| @@ -0,0 +1,36 @@ | ||
| # Copyright 2019 by Sean Luke and George Mason University | ||
| # Licensed under the Academic Free License version 3.0 | ||
| # See the file "LICENSE" for more information | ||
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| ############################## | ||
| # Ant Colony System boiler plate | ||
| ############################## | ||
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| # To implement ACS, we adapt ECJ's steady-state EA model to support local | ||
| # and global pheromone update rules. | ||
| parent.1 = ../../simple/simple.params | ||
| parent.0 = ../../steadystate/steadystate.params | ||
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| breed = ec.steadystate.SteadyStateBreeder | ||
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| # generations = 200 | ||
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| # Population size sets the number of ants per generation | ||
| pop.subpops = 1 | ||
| #pop.subpop.0.size = 20 | ||
| pop.subpop.0.species = ec.co.ant.AntSpecies | ||
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| # To use an ACO algorithm with SteadyStateEvolutionState, we need to tell it to | ||
| # throw away the population after each generation boundary. Otherwise it will try | ||
| # to "insert" ants into a persistent population, which is not how ACO works! | ||
| steady.empty-at-generation-boundary = True | ||
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| ############################## | ||
| # AntSpecies holds the construction and update rules that define ACO algorithms | ||
| ############################## | ||
| pop.subpop.0.species.fitness = ec.simple.SimpleFitness | ||
| pop.subpop.0.species.ind = ec.app.tsp.TSPIndividual | ||
| pop.subpop.0.species.pheromone-table = ec.co.ant.SimplePheromoneTable | ||
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| # Further species parameters (construction-rule, update-rule, etc) must be | ||
| # set by the application. |
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