dependencies: [
    .package(url: "https://github.com/Li-Bot/eakit.git", .upToNextMajor(from: "1.0.0"))
]

// Parameters of Hill Climbing
let parameters = try! EAHillClimbingParameters(
    populationCount: 20,
    generationsCount: 101,
    fitnessFunction: EAAckleyFunction(),
    deviation: 0.5
)
// Run the algorithm
let algorithm = EAHillClimbing(parameters: parameters)
let result = algorithm.run()
// Show result
print(result.bestPopulation.bestIndividual?.fitness)
print(result.bestPopulation.bestIndividual?.data)

// Data of TSP Fitness Function
let cities = [
    EATSPCity("A", [60.0, 200.0]), EATSPCity("B", [80.0, 200.0]), EATSPCity("C", [80.0, 180.0]),
    EATSPCity("D", [140.0, 180.0]), EATSPCity("E", [20.0, 160.0]), EATSPCity("F", [100.0, 160.0]),
    EATSPCity("G", [200.0, 160.0]), EATSPCity("H", [140.0, 140.0]), EATSPCity("I", [40.0, 120.0]),
    EATSPCity("J", [100.0, 120.0]), EATSPCity("K", [180.0, 100.0]), EATSPCity("L", [60.0, 80.0]),
    EATSPCity("M", [120.0, 80.0]), EATSPCity("N", [180.0, 60.0]), EATSPCity("O", [20.0, 40.0]),
    EATSPCity("P", [100.0, 40.0]), EATSPCity("Q", [200.0, 40.0]), EATSPCity("R", [20.0, 20.0]),
    EATSPCity("S", [60.0, 20.0]), EATSPCity("T", [160.0, 20.0])
]
let fitnessFunction = EATSPFunction(cities: cities)
// Parameters of Genetic Algorithm
let parameters = try! EAGeneticAlgorithmParameters(
    populationCount: 20,
    generationsCount: 301,
    fitnessFunction: fitnessFunction,
    isElitism: true,
    selection: EARandomSelection(isElitism: true),
    crossover: EATSPTwoPointCrossover(threshold: 1.0),
    mutation: EASwapMutation(threshold: 0.9, count: 1)
)
// Run the algorithm
let algorithm = EAGeneticAlgorithm(parameters: parameters)
let result = algorithm.run()
// Show result
print(result.bestPopulation.bestIndividual?.fitness)
print(result.bestPopulation.bestIndividual?.data)

// Configuration of Evolutionary Strategy 
let configuration = try! EAEvolutionaryStrategyConfiguration(µ: 20, ρ: 3, selectionStrategy: .plus, λ: 20)
// Parameters of Evolutionary Strategy
let parameters = try! EAEvolutionaryStrategyParameters(
    generationsCount: 101,
    configuration: configuration,
    fitnessFunction: EAAckleyFunction(),
    selection: EARandomSelection(),
    recombination: EAESIntermadiateRecombination(),
    mutation: EAESNormalMutation(threshold: 1.0, σ: 0.5)
)
// Run the algorithm
let algorithm = EAEvolutionaryStrategy(parameters: parameters)
let result = algorithm.run()
// Show result
print(result.bestPopulation.bestIndividual?.fitness)
print(result.bestPopulation.bestIndividual?.data)

// Parameters of Particle Swarm
let parameters = try! EAParticleSwarmParameters(
    particlesCount: 10,
    iterationsCount: 101,
    velocity: EAParticleSwarmVelocity(maximum: EASphereFunction().distance / 30.0),
    learning: .defaultLearning,
    inertiaWeight: .defaultInertiaWeight,
    fitnessFunction: EAAckleyFunction()
)
// Run the algorithm
let algorithm = EAParticleSwarm(parameters: parameters)
let result = algorithm.run()
// Show result
print(result.bestPopulation.bestIndividual?.fitness)
print(result.bestPopulation.bestIndividual?.data)

// Parameters of Differential Evolution
let parameters = try! EADifferentialEvolutionParameters(
    populationCount: 10,
    generationsCount: 101,
    selection: EARandomSelection(),
    mutationStrategy: EADERand1BinMutationStrategy(f: 0.5, λ: 0.5),
    crossover: EADifferentialEvolutionCrossover(cr: 0.9),
    fitnessFunction: EAAckleyFunction()
)
// Run the algorithm
let algorithm = EADifferentialEvolution(parameters: parameters)
let result = algorithm.run()
// Show result
print(result.bestPopulation.bestIndividual?.fitness)
print(result.bestPopulation.bestIndividual?.data)