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Nature-Inspired Algorithms and Applied Optimization

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Cover of 'Nature-Inspired Algorithms and Applied Optimization'

Table of Contents

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    Book Overview
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    Chapter 1 Mathematical Analysis of Nature-Inspired Algorithms
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    Chapter 2 A Review of No Free Lunch Theorems, and Their Implications for Metaheuristic Optimisation
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    Chapter 3 Global Convergence Analysis of Cuckoo Search Using Markov Theory
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    Chapter 4 On Efficiently Solving the Vehicle Routing Problem with Time Windows Using the Bat Algorithm with Random Reinsertion Operators
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    Chapter 5 Variants of the Flower Pollination Algorithm: A Review
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    Chapter 6 On the Hypercomplex-Based Search Spaces for Optimization Purposes
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    Chapter 7 Lévy Flight-Driven Simulated Annealing for B-spline Curve Fitting
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    Chapter 8 A Comprehensive Review of the Flower Pollination Algorithm for Solving Engineering Problems
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    Chapter 9 Bat Algorithm and Directional Bat Algorithm with Case Studies
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    Chapter 10 Applications of Flower Pollination Algorithm in Feature Selection and Knapsack Problems
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    Chapter 11 Why the Firefly Algorithm Works?
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    Chapter 12 An Efficient Computational Procedure for Simultaneously Generating Alternatives to an Optimal Solution Using the Firefly Algorithm
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    Chapter 13 Optimization of Relay Placement in Wireless Butterfly Networks
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    Chapter 14 The Bat Algorithm, Variants and Some Practical Engineering Applications: A Review
Attention for Chapter 2: A Review of No Free Lunch Theorems, and Their Implications for Metaheuristic Optimisation
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