Handbook of AI-based Metaheuristics

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.

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 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 Togan, Tayfun Dede, and Hasan Basri Basaga 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 Teodorovic, Tatjana Davidovic, Milica Šelmic, and Miloš Nikolic 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 Systèmes Intelligents (LiSSi). Anand J Kulkarni is Associate Professor at the Symbiosis Center for Research and Innovation, Symbiosis International (Deemed University).