Handbook of AI-based Metaheuristics

by Anand J. Kulkarni (Editor), Patrick Siarry (Editor)

Overview

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.

Product Details

ISBN-13:	9780367753030
Publisher:	CRC Press
Publication date:	09/02/2021
Series:	Advances in Metaheuristics
Pages:	418
Product dimensions:	6.88(w) x 9.69(h) x (d)

About the Author

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 Systèmes Intelligents (LiSSi).

Anand J Kulkarni is Associate Professor at the Symbiosis Center for Research and Innovation, Symbiosis International (Deemed University).

 

Table of Contents

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, João Batista Monteiro Filho, Murilo Rebelo Pontes,

and João 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

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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 Toğan, Tayfun Dede, and Hasan Basri Başağa

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

Dušan Teodorović, Tatjana Davidović, Milica Šelmić,

and Miloš Nikolić

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