<strong><em> </em></strong> <p>Section I Bio-Inspired Methods</p><p>Chapter 1 Brain Storm Optimization Algorithm</p><i><p>Marwa Sharawi, Mohammadreza Gholami,</p><p>and Mohammed El-Abd</p></i><b></b><p><b>Chapter 2 </b>Fish School Search: Account for the First Decade</p><i><p>Carmelo José Abanez Bastos-Filho, Fernando Buarque de Lima-Neto,</p><p>Anthony José da Cunha Carneiro Lins, Marcelo Gomes Pereira de</p><p>Lacerda, Mariana Gomes da Motta Macedo, Clodomir Joaquim de</p><p>Santana Junior, Hugo Valadares Siqueira, Rodrigo Cesar Lira da Silva,</p><p>Hugo Amorim Neto, Breno Augusto de Melo Menezes, Isabela Maria</p><p>Carneiro Albuquerque, João Batista Monteiro Filho, Murilo Rebelo Pontes,</p><p>and João Luiz Vilar Dias</p></i><b></b><p><b>Chapter 3 </b>Marriage in Honey Bees Optimization in Continuous Domains</p><i><p>Jing Liu, Sreenatha Anavatti, Matthew Garratt,</p><p>and Hussein A. Abbass</p></i><b></b><p><b>Chapter 4 </b>Structural Optimization Using Genetic Algorithm...</p><i><p>Ravindra Desai</p><b><p>Section II Physics and Chemistry-Based Methods</p></b></i><b></b><p><b>Chapter 5 </b>Gravitational Search Algorithm: Theory, Literature Review,</p><p>and Applications</p><i><p>Amin Hashemi, Mohammad Bagher Dowlatshahi,</p><p>and Hossein Nezamabadi-pour</p></i><b></b><p><b>Chapter 6 </b>Stochastic Diffusion Search</p><i><p>Andrew Owen Martin</p></i><p>BK-TandF-KULKARNI-9780367753030-210197-FM.indd 7 22/06/21 2:03 PM</p><b></b><p><b>viii </b>Contents</p><b><i><p>Section III Socio-inspired Methods</p></i></b><p><b>Chapter 7 </b>The League Championship Algorithm: Applications and Extensions</p><p><i>Ali Husseinzadeh Kashan, Alireza Balavand, Somayyeh Karimiyan,</i></p><i><p>and Fariba Soleimani</p></i><b></b><p><b>Chapter 8 </b>Cultural Algorithms for Optimization</p><i><p>Carlos Artemio Coello Coello and Ma Guadalupe Castillo Tapia</p></i><b></b><p><b>Chapter 9 </b>Application of Teaching-Learning-Based Optimization</p><p>on Solving of Time Cost Optimization Problems</p><i><p>Vedat Toğan, Tayfun Dede, and Hasan Basri Başağa</p></i><b></b><p><b>Chapter 10 </b>Social Learning Optimization</p><i><p>Yue-Jiao Gong</p></i><b></b><p><b>Chapter 11 </b>Constraint Handling in Multi-Cohort Intelligence Algorithm</p><p><i>Apoorva S. Shastri and Anand J. Kulkarni</i></p><i><b><p>Section IV Swarm-Based Methods</p></b></i><b></b><p><b>Chapter 12 </b>Bee Colony Optimization and Its Applications</p><i><p>Dušan Teodorović, Tatjana Davidović, Milica Šelmić,</p><p>and Miloš Nikolić</p></i><b></b><p><b>Chapter 13 </b>A Bumble Bees Mating Optimization Algorithm for the Location</p><p>Routing Problem with Stochastic Demands</p><i><p>Magdalene Marinaki and Yannis Marinakis</p></i><b></b><p><b>Chapter 14 </b>A Glowworm Swarm Optimization Algorithm for the Multi-Objective</p><p>Energy Reduction Multi-Depot Vehicle Routing Problem</p><i><p>Emmanouela Rapanaki, Iraklis-Dimitrios Psychas,</p><p>Magdalene Marinaki, and Yannis Marinakis</p></i><b></b><p><b>Chapter 15 </b>Monarch Butterfly Optimization</p><i><p>Liwen Xie and Gai-Ge Wang</p></i>

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. Thiswill be avaluable reference for researchers in industry and academia, as well as for all Master's and PhD students working in the metaheuristics and applications domains.

OCLC-licensed vendor bibliographic record.