Properties
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Represents the default number of replications to be performed during an evaluation.
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Many algorithms compare solutions. This factor serves as the default precision between two solutions such that if the solutions are within this value they are considered equal. The default is 0.001
Functions
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fun createBayesianOptimizationSolver(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, initialDesignSize: Int = BayesianOptimizationSolver.defaultInitialDesignSize, acquisition: AcquisitionFunctionIfc = ExpectedImprovement(), startingPoint: MutableMap<String, Double>? = null, maxIterations: Int = BayesianOptimizationSolver.boDefaultMaxIterations, replicationsPerEvaluation: Int = defaultReplicationsPerEvaluation, solutionCache: SolutionCacheIfc = MemorySolutionCache(), simulationRunCache: SimulationRunCacheIfc? = null, experimentRunParameters: ExperimentRunParametersIfc? = null, streamNum: Int = 0, streamProvider: RNStreamProviderIfc = RNStreamProvider(), name: String? = null, parallelOptions: ParallelEvaluationOptions = ParallelEvaluationOptions()): BayesianOptimizationSolver
Creates and configures a Bayesian optimization (BO) solver for a given problem definition.
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fun createCrossEntropySolver(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, ceSampler: CESampler? = null, startingPoint: MutableMap<String, Double>? = null, maxIterations: Int = CrossEntropySolver.ceDefaultMaxIterations, replicationsPerEvaluation: Int = defaultReplicationsPerEvaluation, solutionCache: SolutionCacheIfc = MemorySolutionCache(), simulationRunCache: SimulationRunCacheIfc? = null, experimentRunParameters: ExperimentRunParametersIfc? = null, streamNum: Int = 0, streamProvider: RNStreamProviderIfc = RNStreamProvider(), name: String? = null, parallelOptions: ParallelEvaluationOptions = ParallelEvaluationOptions()): CrossEntropySolver
Creates and configures a cross-entropy optimization algorithm for a given problem definition.
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fun createGeneticAlgorithmSolver(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, populationSize: Int = GeneticAlgorithmSolver.defaultPopulationSize, selectionOperator: SelectionOperatorIfc = TournamentSelection(), crossoverOperator: CrossoverOperatorIfc = BlendCrossover(), mutationOperator: MutationOperatorIfc? = null, startingPoint: MutableMap<String, Double>? = null, maxIterations: Int = GeneticAlgorithmSolver.gaDefaultMaxIterations, replicationsPerEvaluation: Int = defaultReplicationsPerEvaluation, solutionCache: SolutionCacheIfc = MemorySolutionCache(), simulationRunCache: SimulationRunCacheIfc? = null, experimentRunParameters: ExperimentRunParametersIfc? = null, streamNum: Int = 0, streamProvider: RNStreamProviderIfc = RNStreamProvider(), name: String? = null, parallelOptions: ParallelEvaluationOptions = ParallelEvaluationOptions()): GeneticAlgorithmSolver
Creates and configures a genetic algorithm solver for a given problem definition.
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fun createISCSolver(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, deltaC: Double = problemDefinition.indifferenceZoneParameter, deltaL: Double = deltaC, skipGlobalPhase: Boolean = false, globalBudget: Int? = null, startingPoint: MutableMap<String, Double>? = null, maxIterations: Int = ISCSolver.iscDefaultMaxIterations, replicationsPerEvaluation: Int = defaultReplicationsPerEvaluation, solutionCache: SolutionCacheIfc = MemorySolutionCache(), simulationRunCache: SimulationRunCacheIfc? = null, experimentRunParameters: ExperimentRunParametersIfc? = null, streamNum: Int = 0, streamProvider: RNStreamProviderIfc = RNStreamProvider(), name: String? = null, parallelOptions: ParallelEvaluationOptions = ParallelEvaluationOptions()): ISCSolver
Creates and configures an Industrial Strength COMPASS (ISC) solver for a given problem definition. ISC is a three-phase discrete-optimization-via-simulation method: a global Niching-GA exploration phase, a local COMPASS phase per niche, and a clean-up ranking-and-selection phase.
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fun createParticleSwarmSolver(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, swarmSize: Int = ParticleSwarmSolver.defaultSwarmSize, inertiaSchedule: InertiaWeightScheduleIfc? = null, cognitiveCoefficient: Double = ParticleSwarmSolver.defaultCognitiveCoefficient, socialCoefficient: Double = ParticleSwarmSolver.defaultSocialCoefficient, boundaryHandler: BoundaryHandlerIfc = ClampToBounds(), startingPoint: MutableMap<String, Double>? = null, maxIterations: Int = ParticleSwarmSolver.psoDefaultMaxIterations, replicationsPerEvaluation: Int = defaultReplicationsPerEvaluation, solutionCache: SolutionCacheIfc = MemorySolutionCache(), simulationRunCache: SimulationRunCacheIfc? = null, experimentRunParameters: ExperimentRunParametersIfc? = null, streamNum: Int = 0, streamProvider: RNStreamProviderIfc = RNStreamProvider(), name: String? = null, parallelOptions: ParallelEvaluationOptions = ParallelEvaluationOptions(enabled = true)): ParticleSwarmSolver
Creates and configures a global-best particle swarm optimization (PSO) solver for a given problem definition.
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fun createRandomRestartBayesianOptimizationSolver(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, maxNumRestarts: Int = defaultMaxRestarts, startingPoint: MutableMap<String, Double>? = null, initialDesignSize: Int = BayesianOptimizationSolver.defaultInitialDesignSize, acquisition: AcquisitionFunctionIfc = ExpectedImprovement(), maxIterations: Int = BayesianOptimizationSolver.boDefaultMaxIterations, replicationsPerEvaluation: Int = defaultReplicationsPerEvaluation, solutionCache: SolutionCacheIfc = MemorySolutionCache(), simulationRunCache: SimulationRunCacheIfc? = null, experimentRunParameters: ExperimentRunParametersIfc? = null, streamNum: Int = 0, streamProvider: RNStreamProviderIfc = RNStreamProvider(), name: String? = null, parallelOptions: ParallelEvaluationOptions = ParallelEvaluationOptions()): RandomRestartSolver
Creates and configures a Bayesian optimization solver for a given problem definition that uses a random restart approach.
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fun createRandomRestartCrossEntropySolver(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, maxNumRestarts: Int = defaultMaxRestarts, startingPoint: MutableMap<String, Double>? = null, ceSampler: CESampler? = null, maxIterations: Int = CrossEntropySolver.ceDefaultMaxIterations, replicationsPerEvaluation: Int = defaultReplicationsPerEvaluation, solutionCache: SolutionCacheIfc = MemorySolutionCache(), simulationRunCache: SimulationRunCacheIfc? = null, experimentRunParameters: ExperimentRunParametersIfc? = null, streamNum: Int = 0, streamProvider: RNStreamProviderIfc = RNStreamProvider(), name: String? = null, parallelOptions: ParallelEvaluationOptions = ParallelEvaluationOptions()): RandomRestartSolver
Creates and configures a cross-entropy optimization algorithm for a given problem definition that uses a random restart approach.
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fun createRandomRestartGeneticAlgorithmSolver(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, maxNumRestarts: Int = defaultMaxRestarts, startingPoint: MutableMap<String, Double>? = null, populationSize: Int = GeneticAlgorithmSolver.defaultPopulationSize, selectionOperator: SelectionOperatorIfc = TournamentSelection(), crossoverOperator: CrossoverOperatorIfc = BlendCrossover(), mutationOperator: MutationOperatorIfc? = null, maxIterations: Int = GeneticAlgorithmSolver.gaDefaultMaxIterations, replicationsPerEvaluation: Int = defaultReplicationsPerEvaluation, solutionCache: SolutionCacheIfc = MemorySolutionCache(), simulationRunCache: SimulationRunCacheIfc? = null, experimentRunParameters: ExperimentRunParametersIfc? = null, streamNum: Int = 0, streamProvider: RNStreamProviderIfc = RNStreamProvider(), name: String? = null, parallelOptions: ParallelEvaluationOptions = ParallelEvaluationOptions()): RandomRestartSolver
Creates and configures a genetic algorithm solver for a given problem definition that uses a random restart approach.
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fun createRandomRestartISCSolver(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, maxNumRestarts: Int = defaultMaxRestarts, deltaC: Double = problemDefinition.indifferenceZoneParameter, deltaL: Double = deltaC, skipGlobalPhase: Boolean = false, globalBudget: Int? = null, startingPoint: MutableMap<String, Double>? = null, maxIterations: Int = ISCSolver.iscDefaultMaxIterations, replicationsPerEvaluation: Int = defaultReplicationsPerEvaluation, solutionCache: SolutionCacheIfc = MemorySolutionCache(), simulationRunCache: SimulationRunCacheIfc? = null, experimentRunParameters: ExperimentRunParametersIfc? = null, streamNum: Int = 0, streamProvider: RNStreamProviderIfc = RNStreamProvider(), name: String? = null, parallelOptions: ParallelEvaluationOptions = ParallelEvaluationOptions()): RandomRestartSolver
Creates and configures an Industrial Strength COMPASS (ISC) solver that uses a random restart approach, mirroring the other algorithms' random-restart factories.
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fun createRandomRestartParticleSwarmSolver(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, maxNumRestarts: Int = defaultMaxRestarts, startingPoint: MutableMap<String, Double>? = null, swarmSize: Int = ParticleSwarmSolver.defaultSwarmSize, inertiaSchedule: InertiaWeightScheduleIfc? = null, cognitiveCoefficient: Double = ParticleSwarmSolver.defaultCognitiveCoefficient, socialCoefficient: Double = ParticleSwarmSolver.defaultSocialCoefficient, boundaryHandler: BoundaryHandlerIfc = ClampToBounds(), maxIterations: Int = ParticleSwarmSolver.psoDefaultMaxIterations, replicationsPerEvaluation: Int = defaultReplicationsPerEvaluation, solutionCache: SolutionCacheIfc = MemorySolutionCache(), simulationRunCache: SimulationRunCacheIfc? = null, experimentRunParameters: ExperimentRunParametersIfc? = null, streamNum: Int = 0, streamProvider: RNStreamProviderIfc = RNStreamProvider(), name: String? = null, parallelOptions: ParallelEvaluationOptions = ParallelEvaluationOptions(enabled = true)): RandomRestartSolver
Creates and configures a global-best particle swarm optimization solver for a given problem definition that uses a random restart approach.
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fun createRandomRestartRSplineSolver(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, maxNumRestarts: Int = defaultMaxRestarts, startingPoint: MutableMap<String, Double>? = null, initialNumReps: Int = defaultInitialSampleSize, sampleSizeGrowthRate: Double = defaultReplicationGrowthRate, maxNumReplications: Int = defaultMaxNumReplications, maxIterations: Int = RSplineSolver.rSplineDefaultMaxIterations, replicationsPerEvaluation: Int = defaultReplicationsPerEvaluation, solutionCache: SolutionCacheIfc = MemorySolutionCache(), simulationRunCache: SimulationRunCacheIfc? = null, experimentRunParameters: ExperimentRunParametersIfc? = null, streamNum: Int = 0, streamProvider: RNStreamProviderIfc = RNStreamProvider(), name: String? = null, parallelOptions: ParallelEvaluationOptions = ParallelEvaluationOptions()): RandomRestartSolver
Creates and configures an R-SPLINE optimization algorithm for a given problem definition that uses a random restart approach.
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fun createRandomRestartSimulatedAnnealingSolver(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, maxNumRestarts: Int = defaultMaxRestarts, startingPoint: MutableMap<String, Double>? = null, temperatureConfiguration: TemperatureConfiguration = TemperatureConfiguration.AutoCalibrate(), coolingSchedule: CoolingScheduleIfc = ExponentialCoolingSchedule(defaultInitialTemperature), stoppingTemperature: Double = SimulatedAnnealing.defaultStoppingTemperature, maxIterations: Int = SimulatedAnnealing.saDefaultMaxIterations, replicationsPerEvaluation: Int = defaultReplicationsPerEvaluation, solutionCache: SolutionCacheIfc = MemorySolutionCache(), simulationRunCache: SimulationRunCacheIfc? = null, experimentRunParameters: ExperimentRunParametersIfc? = null, streamNum: Int = 0, streamProvider: RNStreamProviderIfc = RNStreamProvider(), name: String? = null, parallelOptions: ParallelEvaluationOptions = ParallelEvaluationOptions(), concurrentRestarts: Int = 1, concurrentOptions: ConcurrentRunOptions = ConcurrentRunOptions()): RandomRestartSolver
Creates a Random Restart solver that utilizes Simulated Annealing for its inner optimization phases.
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fun createRandomRestartStochasticHillClimberSolver(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, maxNumRestarts: Int = defaultMaxRestarts, startingPoint: MutableMap<String, Double>? = null, maxIterations: Int = StochasticHillClimber.shcDefaultMaxIterations, replicationsPerEvaluation: Int = defaultReplicationsPerEvaluation, solutionCache: SolutionCacheIfc = MemorySolutionCache(), simulationRunCache: SimulationRunCacheIfc? = null, experimentRunParameters: ExperimentRunParametersIfc? = null, streamNum: Int = 0, streamProvider: RNStreamProviderIfc = RNStreamProvider(), name: String? = null, parallelOptions: ParallelEvaluationOptions = ParallelEvaluationOptions(), concurrentRestarts: Int = 1, concurrentOptions: ConcurrentRunOptions = ConcurrentRunOptions()): RandomRestartSolver
Creates and configures a simulated annealing optimization algorithm for a given problem definition.
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fun createRSplineSolver(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, initialNumReps: Int = defaultInitialSampleSize, sampleSizeGrowthRate: Double = defaultReplicationGrowthRate, maxNumReplications: Int = defaultMaxNumReplications, startingPoint: MutableMap<String, Double>? = null, maxIterations: Int = RSplineSolver.rSplineDefaultMaxIterations, solutionCache: SolutionCacheIfc = MemorySolutionCache(), simulationRunCache: SimulationRunCacheIfc? = null, experimentRunParameters: ExperimentRunParametersIfc? = null, streamNum: Int = 0, streamProvider: RNStreamProviderIfc = RNStreamProvider(), name: String? = null, parallelOptions: ParallelEvaluationOptions = ParallelEvaluationOptions()): RSplineSolver
Creates and configures an R-SPLINE optimization algorithm for a given problem definition.
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fun createSimulatedAnnealingSolver(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, startingPoint: MutableMap<String, Double>? = null, temperatureConfiguration: TemperatureConfiguration = TemperatureConfiguration.AutoCalibrate(), coolingSchedule: CoolingScheduleIfc = ExponentialCoolingSchedule(defaultInitialTemperature), stoppingTemperature: Double = SimulatedAnnealing.defaultStoppingTemperature, maxIterations: Int = SimulatedAnnealing.saDefaultMaxIterations, replicationsPerEvaluation: Int = defaultReplicationsPerEvaluation, solutionCache: SolutionCacheIfc = MemorySolutionCache(), simulationRunCache: SimulationRunCacheIfc? = null, experimentRunParameters: ExperimentRunParametersIfc? = null, streamNum: Int = 0, streamProvider: RNStreamProviderIfc = RNStreamProvider(), name: String? = null, parallelOptions: ParallelEvaluationOptions = ParallelEvaluationOptions()): SimulatedAnnealing
Creates a configured Simulated Annealing solver ready for execution.
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fun createStochasticHillClimberSolver(problemDefinition: ProblemDefinition, modelBuilder: ModelBuilderIfc, startingPoint: MutableMap<String, Double>? = null, maxIterations: Int = StochasticHillClimber.shcDefaultMaxIterations, replicationsPerEvaluation: Int = defaultReplicationsPerEvaluation, solutionCache: SolutionCacheIfc = MemorySolutionCache(), simulationRunCache: SimulationRunCacheIfc? = null, experimentRunParameters: ExperimentRunParametersIfc? = null, streamNum: Int = 0, streamProvider: RNStreamProviderIfc = RNStreamProvider(), name: String? = null, parallelOptions: ParallelEvaluationOptions = ParallelEvaluationOptions()): StochasticHillClimber
Creates and configures a stochastic hill climber to solve the given problem definition using a simulation-based evaluation approach. The default configuration has the evaluator configured to use a solution cache.