Description
Taylor & Francis Ltd Swarm Intelligence Principles Advances And Applications 2015 Edition by Aboul Ella Hassanien, Eid Emary
Swarm Intelligence: Principles, Advances, and Applications delivers in-depth coverage of bat, artificial fish swarm, firefly, cuckoo search, flower pollination, artificial bee colony, wolf search, and gray wolf optimization algorithms. The book begins with a brief introduction to mathematical optimization, addressing basic concepts related to swarm intelligence, such as randomness, random walks, and chaos theory. The text then:Describes the various swarm intelligence optimization methods, standardizing the variants, hybridizations, and algorithms whenever possibleDiscusses variants that focus more on binary, discrete, constrained, adaptive, and chaotic versions of the swarm optimizersDepicts real-world applications of the individual optimizers, emphasizing variable selection and fitness function designDetails the similarities, differences, weaknesses, and strengths of each swarm optimization methodDraws parallels between the operators and searching manners of the different algorithmsSwarm Intelligence: Principles, Advances, and Applications presents a comprehensive treatment of modern swarm intelligence optimization methods, complete with illustrative examples and an extendable MATLAB (R) package for feature selection in wrapper mode applied on different data sets with benchmarking using different evaluation criteria. The book provides beginners with a solid foundation of swarm intelligence fundamentals, and offers experts valuable insight into new directions and hybridizations. IntroductionSources of InspirationRandom VariablesPseudo-Random Number GenerationRandom WalkChaosChapter ConclusionBibliographyBat AlgorithmBat Algorithm (BA)BA VariantsBat HybridizationsBA in Real-World ApplicationsChapter ConclusionBibliographyArtificial Fish Swarm AlgorithmFish Swarm OptimizationArtificial Fish Swarm Algorithm (AFSA) VariantsAFSA HybridizationsFish Swarm in Real-World ApplicationsChapter ConclusionBibliographyCuckoo Search AlgorithmCuckoo Search (CS)CS VariantsCS HybridizationsCS in Real-World ApplicationsChapter ConclusionBibliographyFirefly AlgorithmFirefly Algorithm (FFA)FFA VariantFFA HybridizationsFirefly in Real-World ApplicationsChapter ConclusionBibliographyFlower Pollination AlgorithmFlower Pollination Algorithm (FPA)FPA VariantsFPA: HybridizationsReal-World Applications of the FPAFPA in Feature SelectionChapter ConclusionBibliographyArtificial Bee Colony OptimizationArtificial Bee Colony (ABC)ABC VariantsABC HybridizationsABC in Real-World ApplicationsChapter ConclusionBibliographyWolf-Based Search AlgorithmsWolf Search Algorithm (WSA)Wolf Search Optimizers in Real-World ApplicationsChapter ConclusionBibliographyBird's-Eye ViewCriteria (1) Classification According to Swarm GuideCriteria (2) Classification According to the Probability Distribution UsedCriteria (3) Classification According to the Number of Behaviors UsedCriteria (4) Classification According to Exploitation of Positional Distribution of AgentsCriteria (5) Number of Control ParametersCriteria (6) Classification According to Either Generation of Completely New Agents per IterationCriteria (7) Classification Based on Exploitation of Velocity Concept in the OptimizationCriteria (8) Classification According to the Type of Exploration/Exploitation UsedChapter Conclusion