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Discrete Problems in Nature Inspired Algorithms

Author: Anupam Prof Shukla; Ritu Tiwari
Publisher: Boca Raton, FL : CRC Press, 2017.
Edition/Format:   eBook : Document : English : First editionView all editions and formats
Summary:
"This book includes introduction of several algorithms which are exclusively for graph based problems, namely combinatorial optimization problems, path formation problems, etc. Each chapter includes the introduction of the basic traditional nature inspired algorithm and discussion of the modified version for discrete algorithms including problems pertaining to discussed algorithms."--Provided by publisher.
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Genre/Form: Electronic books
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Anupam Prof Shukla; Ritu Tiwari
ISBN: 9781351260886 135126088X 9781351260862 1351260863 9781351260879 1351260871 9781351260855 1351260855
OCLC Number: 1008859353
Description: 1 online resource : text file, PDF
Contents: Cover --
Half Title --
Title Page --
Copyright Page --
Table of Contents --
Foreword I --
Foreword II --
Foreword III --
Preface --
Acknowledgments --
Authors --
1. Introduction to Optimization Problems --
1.1 Introduction --
1.1.1 Artificial Intelligence --
1.1.2 Soft Computing --
1.1.3 Intelligent Systems --
1.1.4 Expert Systems --
1.1.5 Inference Systems --
1.1.6 Machine Learning --
1.1.7 Adaptive Learning --
1.1.8 Related Work Done --
1.1.8.1 Heuristics --
1.1.8.2 Meta-Heuristics --
1.1.8.3 Hyper-Heuristics --
1.1.8.4 Nature-Inspired Computation --
1.1.8.5 Multiagent System --
1.1.8.6 Multiagent Coordination --
1.1.8.7 Multiagent Learning --
1.1.8.8 Learning in Uncertainty --
1.1.8.9 Knowledge Acquisition in Graph-Based Problems Knowledge --
1.2 Combinatorial Optimization Problems --
1.2.1 Traveling Salesman Problem --
1.2.2 Assignment Problem --
1.2.3 Quadratic Assignment Problem --
1.2.4 Quadratic Bottleneck Assignment Problem --
1.2.5 0/1 Knapsack Problem --
1.2.6 Bounded Knapsack Problem --
1.2.7 Unbounded Knapsack Problem --
1.2.8 Multichoice Multidimensional Knapsack Problem --
1.2.9 Multidemand Multidimensional Knapsack Problem --
1.2.10 Quadratic Knapsack Problem --
1.2.11 Sharing Knapsack Problem --
1.2.12 Corporate Structuring --
1.2.13 Sequential Ordering Problem --
1.2.14 Vehicle Routing Problem --
1.2.15 Constrained Vehicle Routing Problem --
1.2.16 Fixed Charge Transportation Problem --
1.2.17 Job Scheduling --
1.2.18 One-Dimensional Bin Packing Problem --
1.2.19 Two-Dimensional Bin Packing Problem --
1.3 Graph-Based Problems --
1.3.1 Graph Coloring Problem --
1.3.2 Path Planning Problem --
1.3.3 Resource Constraint Shortest Path Problem --
1.4 Aim of This Book --
1.5 Summary --
Solved Examples --
Exercises --
References --
2. Particle Swarm Optimization --
2.1 Introduction. 2.2 Traditional Particle Swarm Optimization Algorithm --
2.3 Variants of Particle Swarm Optimization Algorithm --
2.3.1 Sequential Particle Swarm Optimization Algorithm --
2.3.1.1 Random Propagation --
2.3.1.2 Adaptive Schema for Sequential Particle Swarm Optimization Algorithm --
2.3.1.3 Convergence Criterion --
2.3.2 Inertia Weight Strategies in Particle Swarm Optimization Algorithm --
2.3.2.1 Constant Inertia Weight --
2.3.2.2 Random Inertia Weight --
2.3.2.3 Adaptive Inertia Weight --
2.3.2.4 Sigmoid Increasing Inertia Weight --
2.3.2.5 Sigmoid Decreasing Inertia Weight --
2.3.2.6 Linear Decreasing Inertia Weight --
2.3.2.7 The Chaotic Inertia Weight --
2.3.2.8 Chaotic Random Inertia Weight --
2.3.2.9 Oscillating Inertia Weight --
2.3.2.10 Global-Local Best Inertia Weight --
2.3.2.11 Simulated Annealing Inertia Weight --
2.3.2.12 Logarithm Decreasing Inertia Weight --
2.3.2.13 Exponent Decreasing Inertia Weight --
2.3.3 Fine Grained Inertia Weight Particle Swarm Optimization Algorithm --
2.3.4 Double Exponential Self-Adaptive Inertia Weight Particle Swarm Optimization Algorithm --
2.3.5 Double Exponential Dynamic Inertia Weight Particle Swarm Optimization Algorithm --
2.3.6 Adaptive Inertia Weight Particle Swarm Optimization Algorithm --
2.3.7 Chaotic Inertial Weight Approach in Particle Swarm Optimization Algorithm --
2.3.7.1 Application of Chaotic Sequences in Particle Swarm Optimization Algorithm --
2.3.7.2 Crossover Operation --
2.3.8 Distance-Based Locally Informed Particle Swarm Optimization Algorithm --
2.3.8.1 Fitness Euclidean-Distance Ratio Particle Swarm Optimization --
2.3.9 Speciation-Based Particle Swarm Optimization --
2.3.10 Ring Topology Particle Swarm Optimization --
2.3.11 Distance-Based Locally Informed Particle Swarm --
2.3.12 Inertia-Adaptive Particle Swarm Optimization Algorithm with Particle Mobility Factor. 2.3.13 Discrete Particle Swarm Optimization Algorithm --
2.3.14 Particle Swarm Optimization Algorithm for Continuous Applications --
2.4 Convergence Analysis of Particle Swarm Optimization Algorithm --
2.5 Search Capability of Particle Swarm Optimization Algorithm --
2.6 Summary --
Solved Examples --
Exercises --
References --
3. Genetic Algorithms --
3.1 Introduction --
3.2 Encoding Schemes --
3.2.1 Continuous Value Encoding --
3.2.2 Binary Encoding --
3.2.3 Integer Encoding --
3.2.4 Value Encoding or Real Encoding --
3.2.5 Tree Encoding --
3.2.6 Permutation Encoding --
3.3 Selection --
3.3.1 Roulette Wheel Selection --
3.3.2 Rank Selection --
3.3.3 Tournament Selection --
3.3.4 Steady-State Selection --
3.3.5 Random Selection --
3.4 Crossover --
3.4.1 Single Point Crossover --
3.4.2 N Point Crossover --
3.4.3 Uniform Crossover --
3.4.4 Arithmetic Crossover --
3.4.5 Tree Crossover --
3.4.6 Order Changing Crossover --
3.4.7 Shuffle Crossover --
3.5 Mutation --
3.5.1 Inversion Mutation --
3.5.2 Insertion Mutation --
3.5.3 Displacement Mutation --
3.5.4 Reciprocal Exchange Mutation (Swap Mutation) --
3.5.5 Shift Mutation --
3.6 Similarity Template --
3.7 Building Blocks --
3.8 Control Parameters --
3.9 Nontraditional Techniques in GAs --
3.9.1 Genetic Programming --
3.9.2 Discrete Genetic Algorithms --
3.9.3 Genetic Algorithms for Continuous Applications --
3.10 Convergence Analysis of Genetic Algorithms --
3.11 Limitations and Drawbacks of Genetic Algorithms --
3.12 Summary --
Solved Examples --
Exercises --
References --
4. Ant Colony Optimization --
4.1 Introduction --
4.2 Biological Inspiration --
4.2.1 Competition --
4.2.2 High Availability --
4.2.3 Brownian Motion --
4.2.4 Pheromones and Foraging --
4.3 Basic Process and Flowchart --
4.4 Variants of Ant Colony Optimization --
4.4.1 Ant System --
4.4.2 Ant Colony Optimization. 4.4.3 Best-Worst Ant System --
4.4.4 MAX-MIN Ant System --
4.4.5 Rank-Based Ant System --
4.4.6 Ant-Q --
4.4.7 Hyper Cube Ant System --
4.4.8 Mean-Minded Ant Colony Optimization Algorithm --
4.4.8.1 Mathematical Formulations for Mean-Minded Ant Colony Optimization Algorithm --
4.5 Applications --
4.6 Summary --
Solved Examples --
Exercises --
References --
5. Bat Algorithm --
5.1 Biological Inspiration --
5.2 Algorithm --
5.3 Related Work --
Solved Examples --
Exercises --
References --
6. Cuckoo Search Algorithm --
6.1 Introduction --
6.2 Traditional Cuckoo Search Optimization Algorithm --
6.3 Variants of Cuckoo Search Algorithm --
6.3.1 Modified Cuckoo Search --
6.3.2 Improved Cuckoo Search Algorithm with Adaptive Method --
6.3.3 Multiobjective Cuckoo Search Algorithm for Design Optimization --
6.3.3.1 Pareto Front --
6.3.4 Gradient-Based Cuckoo Search for Global Optimization --
6.4 Applications --
6.4.1 Recognition of Parkinson Disease --
6.4.2 Practical Design of Steel Structures --
6.4.3 Manufacturing Optimization Problems --
6.4.4 Business Optimization --
6.4.5 Optimized Design for Reliable Embedded System --
6.4.6 Face Recognition --
6.5 Summary and Concluding Remarks --
Solved Examples --
Exercises --
References --
7. Artificial Bee Colony --
7.1 Introduction --
7.2 Biological Inspiration --
7.3 Swarm Behavior --
7.3.1 ABC Algorithm --
7.4 Various Stages of Artificial Bee Colony Algorithm --
7.5 Related Work --
Solved Examples --
Exercises --
References --
8. Shuffled Frog Leap Algorithm --
8.1 Introduction --
8.2 Related Work Done --
8.2.1 Discrete Shuffled Flog Leaping Algorithm --
8.2.2 Quantum Shuffled Frog Leaping Algorithm --
Solved Questions --
Unsolved Questions --
References --
9. Brain Storm Swarm Optimization Algorithm --
9.1 Introduction --
9.2 Brain Storm Optimization --
9.2.1 Brain Storm Optimization Algorithm. 9.3 Related Work in Brain Storm Optimization and Other Contemporary Algorithms --
9.4 Hybridization of Brain Storm Optimization with Probabilistic Roadmap Method Algorithm --
9.5 Conclusion --
9.6 Future Scope --
Solved Examples --
Exercises --
References --
10. Intelligent Water Drop Algorithm --
10.1 Intelligent Water Drop Algorithm --
10.1.1 Inspiration and Traditional Intelligent Water Drop Algorithm --
10.2 Intelligent Water Drop Algorithm for Discrete Applications --
10.2.1 Intelligent Water Drop Algorithm for an Optimized Route Search --
10.2.2 Intelligent Water Drop Algorithm Convergence and Exploration --
10.3 Variants of Intelligent Water Drop Algorithm --
10.3.1 Adaptive Intelligent Water Drop Algorithm --
10.3.2 Same Sand for Both Parameters (SC1) --
10.3.3 Different Sand for Parameters Same Intelligent Water Drop Can Carry Both (SC2) --
10.3.4 Different Sand for Parameters Same Intelligent Water Drop Cannot Carry Both (SC3) --
10.4 Scope of Intelligent Water Drop Algorithm for Numerical Analysis --
10.5 Intelligent Water Drop Algorithm Exploration and Deterministic Randomness --
10.6 Related Applications --
10.7 Summary --
Solved Question --
Unsolved Questions --
References --
11. Egyptian Vulture Algorithm --
11.1 Introduction --
11.2 Motivation --
11.3 History and Life Style of Egyptian Vulture --
11.4 Egyptian Vulture Optimization Algorithm --
11.4.1 Pebble Tossing --
11.4.2 Rolling with Twigs --
11.4.3 Change of Angle --
11.4.4 Brief Description of the Fitness Function --
11.4.5 Adaptiveness of the Egyptian Vulture Optimization Algorithm --
11.5 Applications of the Egyptian Vulture Optimization Algorithm --
11.5.1 Results of Simulation of Egyptian Vulture Optimization Algorithm over Speech and Gait Set --
Exercises --
Solved Questions --
References --
12. Biogeography-Based Optimization --
12.1 Introduction --
12.2 Biogeography.
Responsibility: editors, Tiwari, Ritu.

Abstract:

"This book includes introduction of several algorithms which are exclusively for graph based problems, namely combinatorial optimization problems, path formation problems, etc. Each chapter includes the introduction of the basic traditional nature inspired algorithm and discussion of the modified version for discrete algorithms including problems pertaining to discussed algorithms."--Provided by publisher.

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