This book 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, as well as newly devised metaheuristic algorithms.
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 Masters and PhD students working in the metaheuristics and applications domains.
Section I Bio-Inspired Methods
Chapter 1 Brain Storm Optimization Algorithm
Marwa Sharawi, Mohammadreza Gholami,
and Mohammed El-Abd
Chapter 2 Fish School Search: Account for the First Decade
Carmelo José Abanez Bastos-Filho, Fernando Buarque de Lima-Neto,
Anthony José da Cunha Carneiro Lins, Marcelo Gomes Pereira de
Lacerda, Mariana Gomes da Motta Macedo, Clodomir Joaquim de
Santana Junior, Hugo Valadares Siqueira, Rodrigo Cesar Lira da Silva,
Hugo Amorim Neto, Breno Augusto de Melo Menezes, Isabela Maria
Carneiro Albuquerque, Joćo Batista Monteiro Filho, Murilo Rebelo Pontes,
and Joćo Luiz Vilar Dias
Chapter 3 Marriage in Honey Bees Optimization in Continuous Domains
Jing Liu, Sreenatha Anavatti, Matthew Garratt,
and Hussein A. Abbass
Chapter 4 Structural Optimization Using Genetic Algorithm...
Ravindra Desai
Section II Physics and Chemistry-Based Methods
Chapter 5 Gravitational Search Algorithm: Theory, Literature Review,
and Applications
Amin Hashemi, Mohammad Bagher Dowlatshahi,
and Hossein Nezamabadi-pour
Chapter 6 Stochastic Diffusion Search
Andrew Owen Martin
BK-TandF-KULKARNI_9780367753030-210197-FM.indd 7 22/06/21 2:03 PM
viii Contents
Section III Socio-inspired Methods
Chapter 7 The League Championship Algorithm: Applications and Extensions
Ali Husseinzadeh Kashan, Alireza Balavand, Somayyeh Karimiyan,
and Fariba Soleimani
Chapter 8 Cultural Algorithms for Optimization
Carlos Artemio Coello Coello and Ma Guadalupe Castillo Tapia
Chapter 9 Application of Teaching-Learning-Based Optimization
on Solving of Time Cost Optimization Problems
Vedat Toan, Tayfun Dede, and Hasan Basri Baaa
Chapter 10 Social Learning Optimization
Yue-Jiao Gong
Chapter 11 Constraint Handling in Multi-Cohort Intelligence Algorithm
Apoorva S. Shastri and Anand J. Kulkarni
Section IV Swarm-Based Methods
Chapter 12 Bee Colony Optimization and Its Applications
Duan Teodorovi, Tatjana Davidovi, Milica elmi,
and Milo Nikoli
Chapter 13 A Bumble Bees Mating Optimization Algorithm for the Location
Routing Problem with Stochastic Demands
Magdalene Marinaki and Yannis Marinakis
Chapter 14 A Glowworm Swarm Optimization Algorithm for the Multi-Objective
Energy Reduction Multi-Depot Vehicle Routing Problem
Emmanouela Rapanaki, Iraklis-Dimitrios Psychas,
Magdalene Marinaki, and Yannis Marinakis
Chapter 15 Monarch Butterfly Optimization
Liwen Xie and Gai-Ge Wang
Patrick Siarry is a Professor of Automatics and Informatics at the University of Paris-Est Créteil, where he leads the Image and Signal Processing team in the Laboratoire Images, Signaux et Systčmes Intelligents (LiSSi).
Anand J Kulkarni is Associate Professor at the Symbiosis Center for Research and Innovation, Symbiosis International (Deemed University).
Biežāk uzdotie jautājumi par e-grāmatām