Discrete Problems in Nature Inspired Algorithms

Content: Chapter 1 Introduction to Optimization Problems1.1 Introduction 1.2 Combinatorial Optimization Problems1.3 Graph Based Problems1.4 Aim of this book1.5 Chapter SummaryReferencesChapter 2 Particle Swarm Optimization (PSO)2.1 Introduction2.2 Traditional Particle Swarm Optimization Algorithm 2.3 Variants of Particle Swarm Optimization Algorithm 2.4 Convergence Analysis of Particle Swarm Optimization Algorithm2.5 Discrete Applications of Particle Swarm Optimization Algorithm2.6 Search Capability of Particle Swarm Optimization AlgorithmQuadratic Assignment Problem:-Chapter SummaryReferencesChapter 3 Genetic Algorithm (GA)3.1 Introduction 3.2 Encoding Schemes3.3 Selection3.4 Crossover3.5 Mutation3.6 Similarity template3.7 Building blocks3.8 Control parameters3.9 Non-traditional techniques in GAS3.10 Convergence Analysis of Genetic Algorithms3.11 Limitations and Drawbacks of Genetic Algorithms3.12 Chapter SummaryReferencesChapter 4 Ant Colony Optimization (ACO)4.1 Introduction 4.2 Biological Inspiration4.3 Basic Process and Flowchart 4.4 Variants of Ant Colony Optimization4.5 Applications 4.6 Chapter Summary ReferencesChapter 5 Bat Algorithm (BA)5.1 Introduction5.2 Biological Inspiration 5.3 Algorithm 5.3 Related Work References ã  ã  ã  ã  ã  Chapter 6 Cuckoo Search Algorithm6.1 Introduction 6.2 Traditional Cuckoo Search Optimization Algorithm 6.3 Variations of Cuckoo Search Algorithm 6.4 Applications6.5 Chapter Summary and Concluding Remarks References ã  Chapter 7 Artificial Bee Colony 7.1 Introduction 7.2 Biological Inspiration7.3 Swarm Behaviour 7.4 Various Stages of ABC Algorithm7.5 Related Work 7.7 References ã  Chapter 8 Shuffled Frog Leap Algorithm 8.1 Introduction 8.2 Related Work Done8.3 Travelling Salesman ProblemReferences ã  Chapter 9 Brain Storm Optimization Algorithm9.1 Introduction 9.2 Working of Brain Storm Optimization Algorithm 9.3 Related Work in BSO and Other Contemporary Algorithms 9.4 Hybridization of BSO with PRMAlgorithm 9.5 Conclusion9.6 Future ScopeReferencesChapter 10 Intelligent Water Drop Algorithm10.1 Intelligent Water Drop Algorithm10.2 Intelligent Water Drop Algorithm for Discrete Applications10.3 Variants of Intelligent Water Drop Algorithm10.4 Scope of Intelligent Water Drop Algorithm for Numerical Analysis10.5 Intelligent Water Drop Algorithm Exploration and Deterministic Randomness 10.6 Related ApplicationsReferencesChapter 11 Egyptian Vulture Algorithm11.1 Introduction 11.2 Motivation11.3 History and Life Style of Egyptian Vulture11.4 EGYPTIAN Vulture Optimization Algorithm11.5 Applications of the EVOA11.6 Referencesã  ã  Chapter 12 Biography Based Optimization (BBO)12.1 Introduction12.2 Bio-geography12.3 Bio-geography based optimization12.4 Bio-geogrpahy based optimization Algorithm12.5 Differnces between BBO and other population based optimization algorithm 12.6 Pseudo-code of the BBO algorithm12.7 Application of BBO12.8 Convergence of Biogeography-based optimization for binary problemsReferencesã  Chapter 13 Invasive Weed Optimization (IWO)13.1 Invasive Weed Optimization13.2 Variants of Invasive weed Optimization13.3 Related work13.4 Chapter SummaryReferencesã  Chapter 14 Glowworm swarm optimization14.1 Introduction14.2 Variants of Glowworm Swarm Optimization Algorithm14.3 Convergence Analysis of Glowworm Swarm Optimization Algorithm14.4 Applications of Glowworm Swarm Optimization Algorithms:14.5 Search Capability of Glowworm Swarm Optimization AlgorithmReferencesã  ã  Chapter 15 Bacteria Foraging Optimization Algorithm15.1 IntroductionBiological Inspiration15.3 Bacterial Foraging Optimization Algorithm 15.4 Variants of BFO with ApplicationsReferences Chapter 16 Flower Pollination Algorithm16.1 Introduction 16.2 Flower Pollination16.3 Characteristics of flower pollination 16.4 Flower Pollination Algorithm (FPA)16.5 Multi-objective Flower Pollination Algorithm 16.6 Variants of Flower Pollination Algorithm16.7 Application of Flower Pollination Algorithm16.8 ConclusionReferencesã