Metaheuristic applications in structures and infrastructures

• Main Authors: Gandomi, Amir Hossein, Yang, Xin-She, Talatahari, Siamak, Alavi, Amir Hossein
• Format: eBook Book
• Language: English
• Published: Amsterdam Elsevier 2013
• Edition: 1
• Series: Elsevier insights
• Subjects: Combinatorial optimization; Engineering mathematics; Heuristic algorithms; Heuristic programming
• ISBN: 9780123983640; 0123983649
• DOI: 10.1016/C2011-0-08778-1

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 t...

Table of Contents:

• References -- 9 Truss Weight Minimization Using Hybrid Harmony Search and Big Bang-Big Crunch Algorithms -- 9.1 Introduction -- 9.2 Statement of the Weight Minimization Problem for a Truss Structure -- 9.3 Harmony Search -- 9.3.1 Generation, Acceptance/Rejection, and Adjustment of a New Harmony -- 9.3.2 Evaluation of the New Trial Design -- 9.3.3 One-Dimensional SA-Type Probabilistic Search -- 9.3.4 Update of the Harmony Memory -- 9.3.5 Termination Criterion -- 9.4 Big Bang-Big Crunch -- 9.4.1 Generation of the Initial Population and Determination of the Center of Mass -- 9.4.2 Evaluation of the Characteristics of the Center of Mass -- 9.4.3 Perturbation of Design Variables -- 9.4.4 Evaluate the Quality of the New Trial Design, Eventually Use Improvement Routines, and Finally Perform a New Explosion -- 9.5 Simulated Annealing -- 9.6 Description of Test Problems -- 9.6.1 Planar 200-Bar Truss Structure Subject to Five Independent Loading Conditions -- 9.6.2 Spatial 3586-Bar Truss Tower -- 9.6.3 Implementation Details -- 9.7 Results of Sensitivity Analysis -- 9.8 Results of the Large-Scale Optimization Problem -- 9.9 Summary and Conclusions -- References -- 10 Graph Theory in Evolutionary Truss Design Optimization -- 10.1 Introduction -- 10.2 Truss Design -- 10.2.1 Equilibrium Equations -- 10.2.2 Formulation of the Optimization Problem -- 10.2.3 Optimization Methods -- 10.3 Graph Theory -- 10.3.1 Basic Terminology -- 10.3.2 Finite Element Representation -- 10.3.3 Weighted Adjacency Matrix -- 10.4 Evolutionary Algorithm -- 10.4.1 Outline -- 10.4.2 Representation -- 10.4.3 Initial Population -- 10.4.4 Kinematic Stability -- 10.4.5 Evaluation -- 10.4.6 Selection -- 10.4.7 Crossover -- 10.4.8 Mutation -- 10.4.9 Replacement -- 10.5 Application -- 10.5.1 Free-Form Tower -- 10.5.2 Bridge Structure -- 10.5.3 Double-Layer Truss Grid -- 10.6 Conclusions
• 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 Programming -- 1.3.2.1.3 Multiexpression Programming -- 1.3.3 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 -- 2 A Review on Traditional and Modern Structural Optimization: Problems and Techniques -- 2.1 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 -- 2.5.8 Firefly Algorithm -- 2.5.9 Cuckoo Search
• 4.4.1 Single-Objective Structural Optimization -- 4.4.2 Multiobjective Structural Optimization -- 4.4.2.1 Criteria and Conflict -- 4.4.2.2 Formulation of a Multiple Objective Optimization Problem -- 4.5 Metaheuristics -- 4.5.1 Solving the Single-Objective Optimization Problems -- 4.5.1.1 Evolution Strategies -- 4.5.1.2 Covariance Matrix Adaptation -- 4.5.1.2.1 Generation of Offsprings -- 4.5.1.2.2 New Mean Value Vector -- 4.5.1.2.3 Global Step Size -- 4.5.1.2.4 Covariance Matrix Update -- 4.5.1.3 Elitist CMA -- 4.5.2 Solving the Multiobjective Optimization Problems -- 4.5.2.1 Nondominated Sorting Evolution Strategies -- 4.5.2.2 Strength Pareto Evolution Strategies -- 4.5.2.3 Multiobjective Elitist Covariance Matrix Adaptation -- 4.6 39-Bar Truss-Test Example -- 4.7 Conclusions -- References -- 5 Multidisciplinary Design and Optimization Methods -- 5.1 Introduction -- 5.2 Coupled Multidisciplinary System -- 5.3 Classifications of MDO Formulations -- 5.4 Single-Level Optimization -- 5.4.1 Multiple-Discipline Feasible -- 5.4.2 All-At-Once Method -- 5.4.3 Individual-Discipline Feasible -- 5.4.4 Comparative Characteristics of Single-Level Optimization -- 5.5 Multilevel Optimization -- 5.5.1 Concurrent Subspace Optimization -- 5.5.2 Bilevel Integrated System Synthesis -- 5.5.3 Collaborative Optimization -- 5.5.3.1 Decomposition of Coupled Systems into CO -- 5.6 Optimization Algorithms -- 5.6.1 Direct Search Methods -- 5.6.2 Gradient-Based Optimization Techniques -- 5.6.3 Metaheuristic Optimization Techniques -- 5.7 High-Fidelity MDO Using Metaheuristic Algorithms -- 5.8 Test Problem -- 5.8.1 Conventional Optimization Problem Formulation -- 5.8.2 CO Formulation -- 5.8.3 Discipline-Level Optimization -- 5.8.4 Implementation of Multi-Fidelity Modeling Methodology in CO -- 5.8.5 System-Level Optimization Using MLSM
• 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 -- 3.4.2 Traffic Flow Forecasting -- 3.4.3 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 -- 3.6.3 Construction Cost Estimation and Prediction -- 3.6.4 Other PSO Applications in Construction Engineering -- 3.7 Geotechnical Engineering -- 3.7.1 Inverse Parameter Identification and Geotechnical Model Calibration -- 3.7.2 Slope Stability Analysis -- 3.8 Pavement Engineering -- 3.9 PSO Applications in Other Civil Engineering Fields -- 3.10 Concluding Remarks -- References -- One: Structural Design -- 4 Evolution Strategies-Based Metaheuristics in Structural Design Optimization -- 4.1 Introduction -- 4.2 Literature Survey -- 4.3 The Structural Optimization Problem -- 4.3.1 Sizing Optimization -- 4.3.2 Shape Optimization -- 4.3.3 Topology Optimization -- 4.4 Problem Formulations
• References
• 5.8.6 Evaluation of Predictive Capabilities of the Metamodels -- 5.8.7 Optimization Algorithms -- 5.9 Conclusions -- References -- 6 Cost Optimization of Column Layout Design of Reinforced Concrete Buildings -- 6.1 Introduction -- 6.2 Statement of the Problem -- 6.3 Formulation in a New Space -- 6.3.1 Slabs -- 6.3.2 Beams -- 6.3.3 Columns -- 6.4 The Optimization Problem -- 6.5 ACO Algorithm for Column Layout Optimization -- 6.5.1 Numerical Example -- 6.6 Conclusions -- References -- 7 Layout Design of Beam-Slab Floors by a Genetic Algorithm -- 7.1 Introduction -- 7.1.1 Heuristic Versus Algorithmic Design Tasks -- 7.1.2 Conversion of Heuristic to Algorithmic Tasks -- 7.1.3 Beam-Slab Layout Design as an Optimization Problem -- 7.2 A Representation of Beam-Slab Layouts -- 7.2.1 A Representation of Beam Locations -- 7.2.2 Elimination of Invalid Beams -- 7.3 A Representative Optimization Problem -- 7.4 A GA for Beam-Slab Layout Design -- 7.4.1 Problem Formulation for a GA -- 7.4.2 Adaptive Penalty and Elitism -- 7.4.3 Algorithm -- 7.5 Examples -- 7.6 Future Challenges -- References -- 8 Optimum Design of Skeletal Structures via Big Bang-Big Crunch Algorithm -- 8.1 Introduction -- 8.2 Statement of the Optimization Design Problem -- 8.2.1 Constraint Conditions for Truss Structures -- 8.2.2 Constraint Conditions for Steel Frames -- 8.2.3 Constraints Handling Approach -- 8.3 Review of the Utilized Methods -- 8.3.1 BB-BC Algorithm -- 8.3.2 Particle Swarm Optimization -- 8.3.3 Sub-Optimization Mechanism -- 8.4 The Proposed Method -- 8.4.1 A Continuous Algorithm -- 8.4.2 A Discrete Algorithm -- 8.5 Design Examples -- 8.5.1 A Square on Diagonal Double-Layer Grid -- 8.5.2 A 26-Story-Tower Spatial Truss -- 8.5.3 A 354-Bar Braced Dome Truss -- 8.5.4 A 582-Bar Tower Truss -- 8.5.5 A 3-Bay 15-Story Frame -- 8.5.6 A 3-Bay 24-Story Frame -- 8.6 Concluding Remarks