Metaheuristics in Water, Geotechnical and Transport Engineering

ISBN-13: 9780323282604 / Angielski / Miękka / 2012 / 504 str.

Xin-She Yang; Amir Hossein Gandomi; Siamak Talatahari

Opis

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

Spis treści

1. Optimization and Metaheuristic Algorithms in Engineering 2.Application of Soft Computing Methods in Water Resources Engineering (Hazi Mohammad Azamathulla)

3.Genetic Algorithms and Their Applications to Water resources Systems

4.Application of Hybrid HS-Solver Algorithm to the Solution of Groundwater Management Problems

5.Evolutionary Multi-objective Optimization of the Water Distribution Networks

6.Ant Colony Optimization for Parameters Estimating of Flood Frequency Distributions

7.Optimal Reservoir Operation for Irrigation Planning Using Swarm Intelligence Algorithm

8.Artificial Intelligence in Geotechnical Engineering: Applications, Modelling Aspects and Future Directions

9.Hybrid heuristic optimization methods in geotechnical engineering

10.Artificial neural network in geotechncial engineering: modelling and application issues

11.Geotechnical Applications of Bayesian Neural Networks

12.Linear and Tree-Based Genetic Programming for Solving Geotechnical Engineering Problems

13.A New Approach to Modelling the Behaviour of Geomaterials

14.Slope Stability analysis using Metaheuristics

15.Scheduling Transportation Networks and Reliability Analysis of Geostructures using Metaheuristics

16.Metaheuristic Applications in Highway and Rail Infrastructure Planning and Design: Implications to Energy and Environmental Sustainability

17.Multi-Objective Optimization of Delay and Stops in Traffic Signal Networks

18.An improved Hybrid Algorithm for Stochastic Bus-Network Design

19.Hybrid method and its application toward smart Pavement Management

O autorze

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 at Middlesex University London, Adjunct Professor at Reykjavik University (Iceland) and Guest Professor at Xi'an Polytechnic University (China). He is an elected Bye-Fellow at Downing College, Cambridge University. He is also the IEEE CIS Chair for the Task Force on Business Intelligence and Knowledge Management, and the Editor-in-Chief of International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO). Dr. Amir H. Gandomi is anARC DECRA Fellow at the Faculty of Engineering andInformation Technology, University of Technology Sydney, Australia. Prior to joining UTS, Dr. Gandomi was an Assistant Professor at Stevens Institute of Technology, USA and a Distinguished Research Fellow in BEACON center, Michigan State University, USA. Dr. Gandomi has published over two hundred journal papers and seven books which collectively have been cited 19,000+ times. He has been named as one of the most influential scientific mindsand Highly Cited Researcher (top 1% publications and 0.1% researchers) for four consecutive years, 2017 to 2020. He also ranked 18th in GP bibliography among more than 12,000 researchers. He has served as associate editor, editor and guest editor in several prestigious journals such as AE of SWEVO, IEEE TBD, and IEEE IoTJ. Dr. Gandomi is active in delivering keynotes and invited talks. His research interests are global optimization andbigdata analytics using Machine Learning and evolutionary computations in particular. Dr. Siamak Talatahari received his Ph.D degree in Structural Engineering from University of Tabriz, Iran. After graduation, he
joined the University of Tabriz where he is presently Professor of Structural Engineering. He is the author of more than 100 papers
published in international journals, 30 papers presented at international conferences and 8 international book chapters. Dr. Talatahari
has been recognized as Distinguished Scientist in the Ministry of Science and Technology and as Distinguished Professor at the
University of Tabriz. He also teaches at the Yakin Dogu University, Nicosia, Cyprus. In addition, he is a co-author with our author
Xin-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 and
Infrastructures, all published by as Insights by Elsevier.