Handbook of AI-based Metaheuristics (Advances in Metaheuristics) 1st Edition
by: Anand J. Kulkarni，Patrick Siarry
Publisher finelybook 出版社: CRC Press; 1st edition (September 2,2021)
Language 语言: English
Print Length 页数: 418 pages
ISBN-10: 0367753030
ISBN-13: 9780367753030
Book Description
At the heart of the optimization domain are mathematical modeling of the problem and the solution methodologies. The problems are becoming larger and with growing complexity. Such problems are becoming cumbersome when handled by traditional optimization methods. This has motivated researchers to resort to artificial intelligence (AI)-based,nature-inspired solution methodologies or algorithms.
The Handbook of AI-based Metaheuristics provides a wide-ranging reference to the theoretical and mathematical formulations of metaheuristics,including bio-inspired,swarm-based,socio-cultural,and physics-based methods or algorithms; their testing and validation,along with detailed illustrative solutions and applications; and newly devised metaheuristic algorithms.
This will be a valuable reference for researchers in industry and academia,as well as for all Master’s and PhD students working in the metaheuristics and applications domains.
Table of contents:
Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Contents
Preface
Editors
List of Contributors
SECTION I: Bio-Inspired Methods
Chapter 1: Brain Storm Optimization Algorithm
Chapter 2: Fish School Search: Account for the First Decade
Chapter 3: Marriage in Honey Bees Optimization in Continuous Domains
Chapter 4: Structural Optimization Using Genetic Algorithm
SECTION II: Physics and Chemistry-Based Methods
Chapter 5: Gravitational Search Algorithm: Theory,Literature Review,and Applications
Chapter 6: Stochastic Diffusion Search
SECTION III: Socio-inspired Methods
Chapter 7: The League Championship Algorithm: Applications and Extensions
Chapter 8: Cultural Algorithms for Optimization
Chapter 9: Application of Teaching-Learning-Based Optimization on Solving of Time Cost Optimization Problems
Chapter 10: Social Learning Optimization
Chapter 11: Constraint Handling in Multi-Cohort Intelligence Algorithm
SECTION IV: Swarm-Based Methods
Chapter 12: Bee Colony Optimization and Its Applications
Chapter 13: A Bumble Bees Mating Optimization Algorithm for the Location Routing Problem with Stochastic Demands
Chapter 14: A Glowworm Swarm Optimization Algorithm for the Multi-Objective Energy Reduction Multi-Depot Vehicle Routing Problem
Chapter 15: Monarch Butterfly Optimization
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