Metaheuristic Algorithms in Modeling and Optimization -- A Review on Traditional and Modern Structural Optimization: Problems and Techniques -- Particle Swarm Optimization in Civil Infrastructure Systems: State-of-the-Art Review -- Evolution Strategies-Based Metaheuristics in Structural Design Optimization -- Multidisciplinary Design and Optimization Methods -- Cost Optimization of Column Layout Design of Reinforced Concrete Buildings -- Layout Design of Beam-Slab Floors by a Genetic Algorithm -- Optimum Design of Skeletal Structures via Big Bang-Big Crunch Algorithm -- Truss Weight Minimization Using Hybrid Harmony Search and Big Bang-Big Crunch Algorithms -- Graph Theory in Evolutionary Truss Design Optimization -- Element Exchange Method for Stochastic Topology Optimization -- Evolutionary Path-Dependent Damper Optimization for Variable Building Stiffness Distributions -- Application of Genetic Algorithms in Ground Motion Selection for Seismic Analysis -- Optimization of Tuned Mass Damper with Harmony Search -- Identification of Passive Devices for Vibration Control by Evolutionary Algorithms -- Structural Optimization for Frequency Constraints -- Optimum Performance-Based Seismic Design of Frames Using Metaheuristic Optimization Algorithms -- Expression Programming Techniques for Formulation of Structural Engineering Systems -- An Evolutionary Divide-and-Conquer Strategy for Structural Identification -- Swarm Intelligence for Large-Scale Optimization in Construction Management -- Network-Level Infrastructure Management Based on Metaheuristics -- Large-Scale Maintenance Optimization Problems for Civil Infrastructure Systems -- Metaheuristic Applications in Bridge Infrastructure Maintenance Scheduling Considering Stochastic Aspects of Deterioration.
Summary
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 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.
