Handbook of AI-based Metaheuristics

Length: 418 pages
Edition: 1
Language: English
Publisher: CRC Press
Publication Date: 2021-09-02
ISBN-10: 0367753030
ISBN-13: 9780367753030
Sales Rank: #0 (See Top 100 Books)

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.

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
Index