05673oam 2200637 450 991082115060332120190911100030.00-12-398379-71-299-19305-6(OCoLC)834561423(MiFhGG)GVRL8DBT(EXLCZ)99267000000033451920130517d2013 uy 0engurun|---uuuuatxtccrMetaheuristic applications in structures and infrastructures /edited by Amir Hossein Gandomi, Civil Engineering, the University of Akron, OH, USA, Xin-She Yang, School of Science and Technology, Middlesex University, London, UK, Siamak Talatahari, Marand Faculty of Engineering, University of Tabriz, Tabriz, Iran, Amir Hossein Alavi, Civil Engineering, Iran University of Science and Technology, Tehran, Iran1st ed.London Elsevier2013London :Elsevier,2013.1 online resource (xx, 556 pages) illustrations (some color)Elsevier insightsGale eBooksElsevier insightsDescription based upon print version of record.0-323-28271-7 0-12-398364-9 Includes bibliographical references.Front Cover; Metaheuristic Applications in Structures and Infrastructures; Copyright Page; Contents; List of Contributors; 1 Metaheuristic Algorithms in Modeling and Optimization; 1.1 Introduction; 1.2 Metaheuristic Algorithms; 1.2.1 Characteristics of Metaheuristics; 1.2.2 No Free Lunch Theorems; 1.3 Metaheuristic Algorithms in Modeling; 1.3.1 Artificial Neural Networks; 1.3.1.1 Multilayer Perceptron Network; 1.3.1.2 Radial Basis Function; 1.3.2 Genetic Programming; 1.3.2.1 Linear-Based GP; 1.3.2.1.1 Linear Genetic Programming; 1.3.2.1.2 Gene Expression Programming1.3.2.1.3 Multiexpression Programming1.3.3 Fuzzy Logic; 1.3.4 Support Vector Machines; 1.4 Metaheuristic Algorithms in Optimization; 1.4.1 Evolutionary Algorithms; 1.4.1.1 Genetic Algorithm; 1.4.1.2 Differential Evolution; 1.4.1.3 Harmony Search; 1.4.2 Swarm-Intelligence-Based Algorithms; 1.4.2.1 Particle Swarm Optimization; 1.4.2.2 Ant Colony Optimization; 1.4.2.3 Bee Algorithms; 1.4.2.4 Firefly Algorithm; 1.4.2.5 Cuckoo Search; 1.4.2.6 Bat Algorithm; 1.4.2.7 Charged System Search; 1.4.2.8 Krill Herd; 1.5 Challenges in Metaheuristics; References2 A Review on Traditional and Modern Structural Optimization: Problems and Techniques2.1 Optimization Problems; 2.2 Optimization Techniques; 2.3 Optimization History; 2.4 Structural Optimization; 2.4.1 General Concept; 2.4.2 Major Advances in Structural Optimization; 2.4.3 OC Methods; 2.4.4 Reliability-Based Optimization Approach; 2.4.5 Fuzzy Optimization; 2.5 Metaheuristic Optimization Techniques; 2.5.1 Genetic Algorithm; 2.5.2 Simulated Annealing; 2.5.3 Tabu Search; 2.5.4 Ant Colony Optimization; 2.5.5 Particle Swarm Optimization; 2.5.6 Harmony Search; 2.5.7 Big Bang-Big Crunch2.5.8 Firefly Algorithm2.5.9 Cuckoo Search; 2.5.10 Other Metaheuristics; References; 3 Particle Swarm Optimization in Civil Infrastructure Systems: State-of-the-Art Review; 3.1 Introduction; 3.2 Particle Swarm Optimization; 3.3 Structural Engineering; 3.3.1 Shape and Size Optimization Problems in Structural Design; 3.3.2 Structural Condition Assessment and Health Monitoring; 3.3.3 Structural Material Characterization and Modeling; 3.3.4 Other PSO Applications in Structural Engineering; 3.4 Transportation and Traffic Engineering; 3.4.1 Transportation Network Design3.4.2 Traffic Flow Forecasting3.4.3 Traffic Control; 3.4.4 Traffic Accident Forecasting; 3.4.5 Vehicle Routing Problem; 3.4.6 Other PSO Application in Transportation and Traffic Engineering; 3.5 Hydraulics and Hydrology; 3.5.1 River Stage Prediction; 3.5.2 Design Optimization of Water/Wastewater Distribution Networks; 3.5.3 Reservoir Operation Problems; 3.5.4 Parameter Estimation/Calibration of Hydrological Models; 3.5.5 Other PSO Applications in Hydraulics and Hydrology; 3.6 Construction Engineering; 3.6.1 Construction Planning and Management; 3.6.2 Construction Litigation3.6.3 Construction Cost Estimation and PredictionDue 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 commonly 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 examinElsevier insights.Engineering designMathematical modelsEngineeringStatistical methodsHeuristic algorithmsEngineering designMathematical models.EngineeringStatistical methods.Heuristic algorithms.620.00151964Gandomi Amir HosseinYang Xin-SheTalatahari SiamakAlavi Amir HosseinScienceDirect (Servicio en línea)MiFhGGMiFhGGBOOK9910821150603321Metaheuristic applications in structures and infrastructures4119718UNINA