This book offers a careful selection of studies in optimization techniques based on artificial intelligence, applied to inverse problems in radiative transfer. In this book, the reader will find an in-depth exploration of heuristic optimization methods, each meticulously described and accompanied by historical context and natural process analogies.
From simulated annealing and genetic algorithms to artificial neural networks, ant colony optimization, and particle swarms, this volume presents a wide range of heuristic methods. Additional approaches such as generalized extreme optimization, particle collision, differential evolution, Luus-Jaakola, and firefly algorithms are also discussed, providing a rich repertoire of tools for tackling challenging problems.
While the applications showcased primarily focus on radiative transfer, their potential extends to various domains, particularly nonlinear and large-scale problems where traditional deterministic methods fall short. With clear and comprehensive presentations, this book empowers readers to adapt each method to their specific needs. Furthermore, practical examples of classical optimization problems and application suggestions are included to enhance your understanding.
This book is suitable to any researcher or practitioner whose interests lie on optimization techniques based in artificial intelligence and bio-inspired algorithms, in fields like Applied Mathematics, Engineering, Computing, and cross-disciplinary areas.
Antônio José da Silva Neto holds a bachelor’s degree in Mechanical and Nuclear Engineering from the Federal University of Rio de Janeiro (1983), a master’s degree in Nuclear Engineering from the same university (1989) and a PhD in Mechanical Engineering from North Carolina State University (1993). He worked for the Brazilian National Nuclear Energy Commission (1984-1986) and Promon Engenharia (1986-1997). In 1997 he joined the Polytechnic Institute of Rio de Janeiro State University, where he has been a professor since 2013. He is also the president (term 2014-2017) of the Brazilian Society for Applied and Computational Mathematics (SBMAC), and a former president (term 2009-2013) of the Brazilian Society of Mechanical Sciences and Engineering (ABCM). His research interests are in two main areas: mechanical engineering, with a focus on heat transfer, and applied and computational mathematics, with a focus on numerical methods and inverse problems.
José Carlos Becceneri has a bachelor’s degree in Applied Mathematics from the University of São Paulo (1978) and a master’s degree and a PhD in Electronic Engineering and Computer Sciences, both from the Technological Institute of Aeronautics (1984). He completed his post-doctoral studies at the University of Sheffield, UK (2000-2001), and from 2011 to 2012 he was Dean of Research and Post-graduate Studies at the National Institute for Space Research (INPE), Brazil. He was also a professor at the post-graduate program in Applied Computing at the Computing and Applied Mathematics Associate Laboratory of the INPE, focusing on projects involving software development and computational intelligence.
Haroldo Fraga de Campos Velho has a bachelor’s degree in Chemical Engineering from the Pontifical Catholic University of Rio Grande do Sul (1983), and a master’s degree (1988) and a PhD (1992) in Mechanical Engineering, with emphasis in Nuclear Reactor Physics and Computational Fluid Dynamics, respectively, both from the Federal University of Rio Grande do Sul. He worked for the Lindolfo Collor Institute for Economic Studies (1983-1985) and was a professor at the University of Caxias do Sul (1987-1988). He has been a researcher at the Computing and Applied Mathematics Laboratory of the National Institute for Space Research (INPE), Brazil, since 1988, and a full researcher since 2009. He was a visiting researcher at the Istituto di Cosmo-geofisica, Italy (1997), and did post-doctoral studies in Atmospheric Sciences at Colorado State University, USA. He is the president of the PanAmerican Association of Computational Interdisciplinary Sciences (term 2014-2016). His research focuses on inverse problems, data assimilation, numerical methods and artificial neural networks.
