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Swarm Intelligence and Evolutionary Computation

Name: Swarm Intelligence and Evolutionary Computation
Author: Georgios Kouziokas
ISBN: 9781000846201

by Georgios Kouziokas

March 2023

Intermediate to advanced

218 pages

4h 17m

English

CRC Press

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Cover Page
Title Page
Copyright Page
Preface
Contents
1. Computational Optimization
Computational optimizationIntroductionOptimization methodsGauss-Newton methodQuasi-Newton methodGradient-based optimizationSteepest or gradient descent algorithmConjugate gradient algorithmsOptimizers for machine learningStochastic gradient descentStochastic gradient descent with momentumLevenberg–Marquardt algorithmScaled conjugate gradient algorithmAdagradRMSPropAdadeltaAdam optimizerNon-gradient methodsReferences
2. Evolutionary Computation and Genetic Algorithm
Evolutionary strategyGenetic algorithmInitializationSelection methodsTournament selectionLinear ranking selectionProportionate roulette wheel selectionExponential ranking selectionCrossover (recombination) operatorsCrossover operators for binary encodingCrossover operators for real-coded genetic algorithmsMutation operatorsGaussian mutationCauchy mutationDiversity mutationLévy flight mutationPower mutationTermination conditionsFitness limitMaximum number of generationsMaximum stall timeMaximum runtimeBest fitness valueAdaptive genetic algorithmDifferential evolutionMutationCrossoverSelectionChaotic differential evolutionDifferential evolution exampleAckley function approximationInput search spaceDifferential evolution parametersReferences
3. Swarm Intelligence and Particle Swarm Optimization
Particle swarm optimization algorithmAcceleration coefficientsInertia weight Stopping criteriaMaximum generation numberMaximum stall timeMaximum runtimeBest fitness valuePopulation convergenceFitness convergenceSwarm topologiesGlobal or fully connectedLocal or ring topologyVon NeumannStar topologyMesh topology Random topologyTree or hierarchical topologyDynamic or adaptive topologiesBoundary handling approachesHyperbolic methodInfinity or invisible wallNearest or boundary or absorb RandomRandom-halfPeriodicExponentialMutationReflect methodsRandom dampingPSO with mutationGaussian mutation PSOCauchy mutation PSOMichalewicz non-uniform mutation Chaotic PSO with Michalewicz mutationRandom mutation PSOConstant mutation PSOStagnant mutationQuantum PSODelta well quantum PSOHarmonic quantum PSOGeometric PSO PSO in neural network optimization PSO as weight optimizerPSO as topology optimizerSwarm optimization examplesSphere functionGriewank function Rastrigin function Ackley function Schwefel function Description References
4. Ant Colony Optimization and Artificial Bee Colony
Ant colony optimization (ACO)IntroductionAnt system (AS)Ant colony system (ACS)Rank-based ant system (RB-AS)Max-min ant system (MMAS) Population-based ACOArtificial bee colony (ABC)Bee colony foraging behaviorABC algorithmSelection methodsDisruptive selectionTournament selectionLinear rank selectionExponential rank selectionBoundary handling approachesReferences
5. Cuckoo Search and Bat Swarm Algorithm
Cuckoo searchCuckoo breeding behavior and Lévy flightsCuckoo search algorithmCuckoo Search algorithm via Levy FlightsCuckoo search variantsChaotic cuckoo searchChaotic Cuckoo Search algorithmDiscrete binary cuckoo searchHybrid self-adaptive cuckoo searchSelf-adaptation of the control parametersPopulation reductionBat algorithmBat algorithm inspirationBat AlgorithmBat movementBat algorithm variantsBinary bat algorithmChaotic bat algorithm1st Chaotic bat algorithm2nd Chaotic bat algorithm3rd Chaotic bat algorithmSelf-adaptive bat algorithmStep-control mechanismMutation mechanismBat algorithm with double mutationTime factor modificationCauchy mutation operator modificationGaussian mutation operatorReferences

6. Firefly Algorithm, Harmony Search and Cat Swarm Algorithm
Firefly algorithmFirefly AlgorithmFirefly algorithm variantsFirefly algorithm with Lévy flightsFirefly AlgorithmChaotic firefly algorithmsHarmony search algorithmHarmony search algorithmHarmony search exampleHarmony search variantsImproved harmony search algorithmChaotic harmony searchCat swarm optimizationCat swarm algorithmBasic description of Cat Swarm AlgorithmCat algorithm variantsBinary discrete Cat algorithmImproved cat swarm optimizationReferences
7. Grey Wolf, Whale and Grasshopper Optimization
Grey wolf optimizationEncircling preyHuntingAttacking prey (exploitation)Search for prey (exploration)Grey Wolf Optimization AlgorithmGrey wolf algorithm variantsBinary grey wolf optimizationBinary grey wolf optimization method IBinary grey wolf optimization method IIGrey wolf with Lévy flightWhale optimization algorithmEncircling preyBubble-net attacking strategy (exploitation phase)Whale optimization variantsWhale optimization with Lévy flightLévy Flight Whale optimization algorithmBinary whale optimization algorithmGrasshopper optimization algorithmGrasshopper Optimization AlgorithmGrasshopper optimization variantsChaotic grasshopper optimization algorithmImproved grasshopper optimization algorithmReferences
8. Machine Learning Optimization Applications
Artificial neural networksWeight optimization of a neural networkTopology optimization of a neural networkNeural network training with PSO, ACO, GAExperimental setupFeature selection with swarm intelligence and genetic algorithmProblem definitionData analysis in machine learningEnergy consumption datasetData pre-processingNormalizationProcessing dataset outliersCost-based feature selection with swarm intelligenceCorrelation-based feature selection with swarm intelligenceExperimental setupGenetic algorithmGeometric PSOChaotic harmony searchChaotic Cuckoo SearchEvolutionary algorithmPredictions with reduced features, SVM and random forestCrime forecasting with PSO-SVMReferences
9. Swarm and Evolutionary Intelligence in Deep Learning
Deep LSTM and Bi-LSTM networksDeep CNN (Convolutional Neural Networks)CNN and LSTM optimization Topology optimization Weight optimizationExperiments Bi-LSTM optimizationObjective function Experimental setupCNN optimization CNN – PSO modelEvaluation metricsCovid-19 chest X-ray datasetExperimental resultsReferences
Index

Content preview from Swarm Intelligence and Evolutionary Computation

Chapter 8 Machine Learning Optimization Applications

This chapter is devoted to machine learning optimization applications, such as artificial neural network optimization. A neural network can be optimized by using swarm and evolutionary optimization methods in several ways, such as: weight optimization, structure optimization, feature selection with evolutionary algorithms. Illustrative real-world examples are presented with real datasets, by using several methods to optimize a neural network such as: genetic algorithm, particle swarm optimization and ant colony optimization.

Artificial neural networks

The Artificial Neural Networks (ANNs) are artificial computational systems that simulate the neural architecture of a human brain. A neural ...

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Swarm Intelligence and Evolutionary Computation

by Georgios Kouziokas

Chapter 8 Machine Learning Optimization Applications

Artificial neural networks

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