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020 _a9781611223989
_q(electronic bk.)
020 _z9781611220230
035 _a(MiAaPQ)EBC3017986
035 _a(Au-PeEL)EBL3017986
035 _a(CaPaEBR)ebr10658908
035 _a(OCoLC)744654303
040 _aMiAaPQ
_beng
_erda
_epn
_cMiAaPQ
_dMiAaPQ
050 4 _aQ337.3 -- .A565 2011eb
082 0 _a006.3
100 1 _aSun, Emily C.
245 1 0 _aAnt Colonies :
_bBehavior in Insects and Computer Applications.
250 _a1st ed.
264 1 _aHauppauge :
_bNova Science Publishers, Incorporated,
_c2011.
264 4 _c©2011.
300 _a1 online resource (286 pages)
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aComputer Science, Technology and Applications
505 0 _aIntro -- ANT COLONIES: BEHAVIOR IN INSECTS AND COMPUTER APPLICATIONS -- ANT COLONIES: BEHAVIOR IN INSECTS AND COMPUTER APPLICATIONS -- CONTENTS -- PREFACE -- Chapter 1 PROGRESSIVE ORGANIZATION OF CO-OPERATING COLONIES/COLLECTIONS OF ANTS/AGENTS (POOCA) FOR COMPETENT PHEROMONE-BASED NAVIGATION AND MULTI-AGENT LEARNING -- Abstract -- 1. Introduction -- 2. Robot Navigation -- 3. Real Ants Colonies - Ant-Inspired Navigation and Optimization -- 3.1. Ant Foraging - Stigmetry -- 3.1.1. Independent navigation employing long-term memory -- 3.1.2. Cooperative navigation and pheromone trail following -- 3.2. Ant-Inspired Navigation - Ant Colony Optimization - Multi-Agent Systems - Swarm Robotics -- 3.2.1. Swarm intelligence and ant colony optimization - Swarm robotics -- 3.2.2. Co-Operative multi agent systems -- 4. Progressive Optimization of Organized Colonies of Ants (POOCA) -- 4.1. POOCA Principles -- 4.2. POOCA Problem Representation -- 4.3. POOCA Operation -- 4.4. POOCA Trail Optimization via Evolution -- 5. Navigation Problems -- 5.1. Static Problems -- 5.1.1. Obstacle-free navigation -- 5.1.2. Navigation in environments cluttered with obstacles -- 5.2. Dynamic Environments -- 6. Future Research -- 7. Conclusion -- Acknowledgments -- Dedication -- References -- Chapter 2 ANT COLONY SOLUTION TO THE OPTIMAL TRANSFORMER SIZING AND EFFICIENCY PROBLEM IN POWER SYSTEMS -- Abstract -- 1. Introduction -- 2. Overview of the Proposed Method -- 2.1. Optimal Transformer Sizing -- 2.2. Optimal Transformer Efficiency Selection -- 3. Calculation of Transformer Thermal Loading -- 3.1. Top-Oil Temperature Calculation -- 3.2. Winding Hottest Spot Temperature Calculation -- 3.3. Insulation Aging -- 3.4. Overloading Capability -- 4. Calculation of Transformer Energy Loss Cost -- 5. Elitist Ant System Method -- 5.1. Mechanism of EAS Algorithm.
505 8 _a5.2. OTS Implementation Using the EAS Algorithm -- 6. Application of ACO Algorithm to Optimal Transformer Sizing Problem -- 7. Application of ACO Algorithm to Optimal Transformer Efficiency Selection Problem -- 8. Implementation of ACO Algorithm in Matlab -- 9. Conclusions -- References -- Chapter 3 DISTRIBUTED DECISIONS: NEW INSIGHTS FROM RADIO-TAGGED ANTS -- Abstract -- Introduction -- Distributed Decisions: Ants as a Model System -- The Role of RFID Technology -- Methodology -- RFID Technology -- Application to Ants -- Distributed Decisions -- Case Study 1: Task-Allocation -- Case Study 2: Nest Choice and Emigration -- Conclusion -- Future Directions -- Distributed Decisions -- Acknowledgments -- References -- Chapter 4 IMPACTS, ECOLOGY AND DISPERSAL OF THE INVASIVE ARGENTINE ANT -- Abstract -- Introduction -- The Argentine Ant Linepithema Humile: Pest Status -- Distribution and Habitat -- Impacts -- 1) Impacts on Ecosystems -- 2) Impacts on Agriculture -- 3) Impacts in Urban Area -- Ecology -- 1) Polygyny [Multi-Queen System] -- 2) Colony Budding -- 3) Opportunistic Nesting Behavior -- 4) Supercoloniality -- 5) Broad Dietary Spectrum -- Control -- Dispersal -- Means of Dispersal -- Keys to Infer Dispersal History of Argentine Ants: Relevance of Supercolony Identity -- Dispersal History -- Conclusion -- Acknowledgments -- References -- Chapter 5 ANT COLONY OPTIMIZATION USED IN NO WAVEFRONT SENSOR ADAPTIVE OPTICS SYSTEMS FOR SOLID-STATE LASERS -- Abstract -- 1. Introduction -- 2. Ant Colony Algorithm -- 4. Simulations and Results -- 5. Conclusion -- References -- Chapter6ANTCOLONYOPTIMIZATIONAGENTSANDPATHROUTING:THECASESOFCONSTRUCTIONSCHEDULINGANDURBANWATERDISTRIBUTIONPIPENETWORKS -- Abstract -- 1.Introduction -- 2.AntColonyOptimization -- 2.1.TheACOMetaheuristic -- 3.CaseStudy1:Resource-UnconstrainedConstructionScheduling.
505 8 _a3.1.ACO-basedAlgorithm -- 3.2.ConstructionProjectExample -- 3.2.1.TheACOApproach -- 4.CaseStudy2:RoutingofPipingNetworks -- 4.1.ACO-BasedAlgorithm -- 4.2.AMoreComplicatedCaseStudy -- 4.2.1.ACO-BasedSolution -- 5.ComparingACOwithotherPathSearchTechniques -- 6.Conclusion -- References -- Chapter7KANTS:ASELF-ORGANIZEDANTSYSTEMFORPATTERNCLUSTERINGANDCLASSIFICATION -- Abstract -- 1.Introduction -- 2.PreliminaryConcepts -- 2.1.ACO -- 2.2.SOM -- 2.3.AntSystemModel -- 3.Self-OrganizingAntsModel -- 3.1.DecideWheretoGoRule -- 3.2.TheUpdatingFunction -- 3.3.TheEvaporationFunction -- 3.4.Pseudocode -- 4.ExperimentsandResults -- 4.1.TheDatasets -- 4.2.Clustering -- 4.3.Classification -- 5.Conclusion -- References -- Chapter8AHYBRIDSYSTEMBASEDINANTCOLONYANDPARACONSISTENTLOGIC -- Abstract -- 1.Introduction -- 2.AntColonyOptimization -- 2.1.CombinatorialOptimizationProblems -- 2.1.1.HeuristicAlgorithms -- Metaheuristics -- 2.2.BiologicalInspiration -- 2.3.Pseudo-codeoftheACOMetaheuristic -- 2.3.1.TheConstructionofSolutionsbyAnts -- 2.3.2.UpdatingthePheromone -- 2.3.3.VariationsoftheACOMetaheuristic -- 3.ParaconsistentLogic -- 3.1.ClassicalLogic -- 3.2.Non-classicalLogic -- 3.3.HistoryofParaconsistentLogic -- 3.4.FoundationsofParaconsistentLogic -- ParaconsistentLogicModelingHumanKnowledge -- 3.4.1.PropositionalParaconsistentAnnotatedLogicP˝ -- 3.4.2.RepresentationofLatticesofAnnotatedParaconsistentLogic -- 3.4.3.ParaconsistentAnnotatedLogicwithAnnotationofTwoValuesPAL2v -- 3.4.4.AnotherInterpretationofPAL2v -- 3.4.5.TheExtentoftheParaconsistentAnnotatedLogicofThreeVariables -- 4.HybridSystem=PAL+ACO -- 4.1.ExperimentalResults -- 5.Conclusion -- References -- Chapter 9 ANT COLONY OPTIMIZATION: A POWERFUL STRATEGY FOR BIOMARKER FEATURE SELECTION* -- Abstract -- Introduction -- Conclusion -- Acknowledgments -- References.
505 8 _aChapter 10 ANT COLONY OPTIMIZATION BASED MESSAGE AUTHENTICATION FOR WIRELESS NETWORKS* -- Abstract -- 1. Introduction -- 2. Cellular Network System -- 3. Ant Colony Optimization -- 4. System Model and Key Distribution Scheme -- 5. Mark Generation in Packets Using Ant Colony Optimization Based Boolean Function Minimization -- 5.1. Model of an Ant System -- 6. ABXE Algorithm-Construction and Design -- 6.1. Ant Agent Representation of the Boolean Expression -- Assignment of Energy Value -- Computation of Energy Value for a Large Number of Packets in a Group -- 6.4. Algorithm: Ant Colony Optimized Boolean Expression Evolver -- 6.4. Algorithm: Ant Colony Optimized Boolean Expression Evolver -- 7. Experimental Results -- 8. Comparison with Existing Methods -- 8.1. Merkle Tree Approach -- 8.2. ACO Based Message Authentication -- 9. Conclusion -- Acknowledgment -- References -- INDEX -- Blank Page.
588 _aDescription based on publisher supplied metadata and other sources.
590 _aElectronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
650 0 _aSwarm intelligence.
650 0 _aAnts -- Nests.
650 0 _aAnts -- Behavior.
655 4 _aElectronic books.
776 0 8 _iPrint version:
_aSun, Emily C.
_tAnt Colonies: Behavior in Insects and Computer Applications
_dHauppauge : Nova Science Publishers, Incorporated,c2011
_z9781611220230
797 2 _aProQuest (Firm)
830 0 _aComputer Science, Technology and Applications
856 4 0 _uhttps://ebookcentral.proquest.com/lib/orpp/detail.action?docID=3017986
_zClick to View
999 _c59378
_d59378