UID:
almahu_9949386246302882
Umfang:
1 online resource
ISBN:
9781000076646
,
1000076644
,
9780429289071
,
0429289073
,
9781000076608
,
1000076601
,
9781000076622
,
1000076628
Inhalt:
Nature-Inspired Optimization Algorithms, a comprehensivework on the most popular optimization algorithms based on nature, starts with an overview of optimizationgoing from the classical to the latest swarm intelligence algorithm. Nature has a rich abundance of flora and fauna that inspired the development of optimization techniques, providing us with simple solutions to complex problems in an effective and adaptive manner. The study of the intelligent survival strategies of animals, birds, and insects in a hostile and ever-changing environment has led to the development of techniques emulating their behavior. This book is a lucid description of fifteen important existing optimization algorithms based on swarm intelligence and superior in performance. It is a valuable resource for engineers, researchers, faculty, and students who are devising optimum solutions to any type of problem rangingfrom computer science to economics andcovering diverse areas that require maximizing output and minimizing resources. This is the crux of all optimization algorithms. Features: Detailed description of the algorithms along with pseudocode and flowchart Easy translation to program code that is also readily available in Mathworks website for some of the algorithms Simple examples demonstrating the optimization strategies are provided to enhance understanding Standard applications and benchmark datasets for testing and validating the algorithms are included This book is a reference for undergraduate and post-graduate students. It will be useful to faculty members teaching optimization. It is also a comprehensive guide for researchers who are looking for optimizing resources in attaining the best solution to a problem. The nature-inspired optimization algorithms are unconventional, and this makes them more efficient than their traditional counterparts.
Anmerkung:
Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Author -- 1 Introduction -- 1.1 Introduction -- 1.2 Fundamentals of Optimization -- 1.3 Types of Optimization Problems -- 1.4 Examples of Optimization -- 1.5 Formulation of Optimization Problem -- 1.6 Classification of Optimization Algorithms -- 1.7 Traveling Salesman Problem and Knapsack Problem -- 1.8 Summary -- 2 Classical Optimization Methods -- 2.1 Introduction -- 2.2 Mathematical Model of Optimization -- 2.3 Linear Programming -- 2.3.1 Simplex Method -- 2.3.2 Revised Simplex Method
,
2.3.3 Kamarkar's Method -- 2.3.4 Duality Theorem -- 2.3.5 Decomposition Principle -- 2.3.6 Transportation Problem -- 2.4 Non-Linear Programming -- 2.4.1 Quadratic Programming -- 2.4.2 Geometric Programming -- 2.5 Dynamic Programming -- 2.6 Integer Programming -- 2.7 Stochastic Programming -- 2.8 Lagrange Multiplier Method -- 2.9 Summary -- References -- 3 Nature-Inspired Algorithms -- 3.1 Introduction -- 3.2 Traditional versus Nature-Inspired Algorithms -- 3.3 Bioinspired Algorithms -- 3.4 Swarm Intelligence -- 3.5 Metaheuristics -- 3.6 Diversification and Intensification
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3.7 No Free Lunch Theorem -- 3.8 Parameter Tuning and Control -- 3.9 Algorithm -- 3.10 Pseudocode -- 3.11 Summary -- References -- 4 Genetic Algorithm -- 4.1 Introduction -- 4.2 Basics of Genetic Algorithm -- 4.3 Genetic Operators -- 4.4 Example of GA -- 4.5 Algorithm -- 4.6 Pseudocode -- 4.7 Schema Theory -- 4.8 Prisoner's Dilemma Problem -- 4.9 Variants and Hybrids of GA -- 4.10 Summary -- References -- 5 Genetic Programming -- 5.1 Introduction -- 5.2 Basics of Genetic Programming -- 5.3 Data Structures for Genetic Programming -- 5.4 Binary Tree Traversals -- 5.5 Genetic Programming Operators
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5.6 Genetic Programming Algorithm -- 5.7 Pseudocode -- 5.8 Variants of the Algorithm -- 5.9 Summary -- References -- 6 Particle Swarm Optimization -- 6.1 Introduction -- 6.2 Swarm Behavior -- 6.3 Particle Swarm Optimization -- 6.3.1 Algorithm -- 6.3.2 Pseudocode -- 6.4 Variants of the Algorithm -- 6.5 Summary -- References -- 7 Differential Evolution -- 7.1 Introduction -- 7.2 Differential Evolution -- 7.2.1 Algorithm -- 7.2.2 Pseudocode -- 7.3 Variants of the Algorithm -- 7.4 Summary -- References -- 8 Ant Colony Optimization -- 8.1 Introduction -- 8.2 Ant Colony Characteristics
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8.3 Ant Colony Optimization -- 8.3.1 Traveling Salesman Problem -- 8.3.2 Algorithm -- 8.3.3 Pseudocode -- 8.4 Variants of the Algorithm -- 8.5 Summary -- References -- 9 Bee Colony Optimization -- 9.1 Introduction -- 9.2 Honey Bee Characteristics -- 9.3 Bee Colony Optimization -- 9.3.1 Algorithm -- 9.3.2 Pseudocode -- 9.4 Variants of the Algorithm -- 9.5 Summary -- References -- 10 Fish School Search Algorithm -- 10.1 Introduction -- 10.2 Fish School Behavior -- 10.3 Fish School Search Optimization -- 10.3.1 Algorithm -- 10.3.2 Pseudocode -- 10.4 Variants and Applications -- 10.5 Summary
Weitere Ausg.:
Print version: ISBN 0367503298
Weitere Ausg.:
ISBN 9780367503291
Sprache:
Englisch
Schlagwort(e):
Electronic books.
;
Electronic books.
URL:
https://www.taylorfrancis.com/books/9780429289071
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