LEADER 00972nam a2200241 i 4500 001 991000523569707536 005 20020509171804.0 008 960614s1990 ||| ||| | ||| 020 $a0195061888 035 $ab11371195-39ule_inst 035 $aPARLA210228$9ExL 040 $aDip.to scienze storiche$bita 100 1 $aMendelsohn, Ezra$0298491 245 10$aStudies in contemporary Jewryan annual 6. /$cedited by Ezra Mendelsohn 260 $aNew York-Oxford :$bOxford university press,$c1990 300 $aXIII, 418 p. ;$c24 cm. 500 $aIn testa al front.: Institute of contemporary Jewry, the Hebrew University of Jerusalem 907 $a.b11371195$b21-09-06$c01-07-02 912 $a991000523569707536 945 $aLE009 Stor. 79.1-107e$g1$iLE009-3047$lle009$n6$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i11553534$z01-07-02 996 $aStudies in contemporary Jewryan annual 6$9816223 997 $aUNISALENTO 998 $ale009$b01-01-96$cm$da $e-$feng$gxx $h0$i1 LEADER 05673oam 2200637 450 001 9910786137003321 005 20190911100030.0 010 $a0-12-398379-7 010 $a1-299-19305-6 035 $a(OCoLC)834561423 035 $a(MiFhGG)GVRL8DBT 035 $a(EXLCZ)992670000000334519 100 $a20130517d2013 uy 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt 182 $cc 183 $acr 200 00$aMetaheuristic applications in structures and infrastructures /$fedited 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, Iran 205 $a1st ed. 210 $aLondon $cElsevier$d2013 210 1$aLondon :$cElsevier,$d2013. 215 $a1 online resource (xx, 556 pages) $cillustrations (some color) 225 1 $aElsevier insights 225 0 $aGale eBooks 225 0$aElsevier insights 300 $aDescription based upon print version of record. 311 $a0-323-28271-7 311 $a0-12-398364-9 320 $aIncludes bibliographical references. 327 $aFront 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 Programming 327 $a1.3.2.1.3 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; References 327 $a2 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 Crunch 327 $a2.5.8 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 Design 327 $a3.4.2 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 Litigation 327 $a3.6.3 Construction Cost Estimation and Prediction 330 $aDue 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. 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