Metaheuristics in Water, Geotechnical and Transport Engineering

Xin-She Yang · Siamak Talatahari · Amir Hossein Alavi

Dec 2012 · Newnes

Ebook

496

Pages

Eligible

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About this ebook

Due to an ever-decreasing supply in raw materials and stringent constraints on conventional energy sources, demand for lightweight, efficient and low cost structures has become crucially important in modern engineering design. This requires engineers to search for optimal and robust design options to address design problems that are often large in scale and highly nonlinear, making finding solutions challenging. In the past two decades, metaheuristic algorithms have shown promising power, efficiency and versatility in solving these difficult optimization problems. This book examines the latest developments of metaheuristics and their applications in water, geotechnical and transport engineering offering practical case studies as examples to demonstrate real world applications. Topics cover a range of areas within engineering, including reviews of optimization algorithms, artificial intelligence, cuckoo search, genetic programming, neural networks, multivariate adaptive regression, swarm intelligence, genetic algorithms, ant colony optimization, evolutionary multiobjective optimization with diverse applications in engineering such as behavior of materials, geotechnical design, flood control, water distribution and signal networks. This book can serve as a supplementary text for design courses and computation in engineering as well as a reference for researchers and engineers in metaheursitics, optimization in civil engineering and computational intelligence. - Provides detailed descriptions of all major metaheuristic algorithms with a focus on practical implementation - Develops new hybrid and advanced methods suitable for civil engineering problems at all levels - Appropriate for researchers and advanced students to help to develop their work

About the author

Xin-She Yang obtained his DPhil in Applied Mathematics from the University of Oxford. He then worked at Cambridge University and National Physical Laboratory (UK) as a Senior Research Scientist. He is currently a Reader in Modelling and Simulation at Middlesex University London, Fellow of the Institute of Mathematics and its Application (IMA) and a Book Series Co-Editor of the Springer Tracts in Nature-Inspired Computing. He has published more than 25 books and more than 400 peer-reviewed research publications with over 82000 citations, and he has been on the prestigious list of highly cited researchers (Web of Sciences) for seven consecutive years (2016-2022).

Dr. Siamak Talatahari received his Ph.D degree in Structural Engineering from University of Tabriz, Iran. After graduation, hejoined the University of Tabriz where he is presently Professor of Structural Engineering. He is the author of more than 100 paperspublished in international journals, 30 papers presented at international conferences and 8 international book chapters. Dr. Talataharihas been recognized as Distinguished Scientist in the Ministry of Science and Technology and as Distinguished Professor at theUniversity of Tabriz. He also teaches at the Yakin Dogu University, Nicosia, Cyprus. In addition, he is a co-author with our authorXin-She Yang of Swarm Intelligence and Bio-Inspired Computation: Structural Optimization Using Krill Herd Algorithm;Metaheuristics in Water, Geotechnical and Transport Engineering, and Metaheuristic Applications in Structures andInfrastructures, all published by as Insights by Elsevier.

Amir Hossein Alavi is an Assistant Professor in the Department of Civil and Environmental Engineering, and holds courtesy appointments in the Department of Bioengineering and Department of Mechanical Engineering and Materials Science, at the University of Pittsburgh, United States. His multidisciplinary scientific studies are organized around three research thrusts: 1) mechanics and electronics of multifunctional materials and structures, 2) embedded self-powered sensing systems, and 3) data-driven characterization, design and discovery of engineering systems.

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