Introduction to artificial life
Introduction to artificial life
Swarm intelligence
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An Evolutionary Algorithm for Integer Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Ant Colony Optimization
Recent Developments In Biologically Inspired Computing
Recent Developments In Biologically Inspired Computing
Particle swarm approach for structural design optimization
Computers and Structures
Discrete optimum design of truss structures using artificial bee colony algorithm
Structural and Multidisciplinary Optimization
Soil-structure interaction: Parameters identification using particle swarm optimization
Computers and Structures
Ant colony optimization of irregular steel frames including elemental warping effect
Advances in Engineering Software
Improved harmony search algorithms for sizing optimization of truss structures
Computers and Structures
Structural and Multidisciplinary Optimization
An exponential big bang-big crunch algorithm for discrete design optimization of steel frames
Computers and Structures
Upper bound strategy for metaheuristic based design optimization of steel frames
Advances in Engineering Software
Sizing truss structures using teaching-learning-based optimization
Computers and Structures
A new optimization method: Dolphin echolocation
Advances in Engineering Software
Fully Stressed Design Evolution Strategy for Shape and Size Optimization of Truss Structures
Computers and Structures
Hybrid fuzzy-genetic system for optimising cabled-truss structures
Advances in Engineering Software
A bat-inspired algorithm for structural optimization
Computers and Structures
Advances in Engineering Software
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In recent years a number of metaheuristic search techniques have been widely used in developing structural optimization algorithms. Amongst these techniques are genetic algorithms, simulated annealing, evolution strategies, particle swarm optimizer, tabu search, ant colony optimization and harmony search. The primary goal of this paper is to objectively evaluate the performance of abovementioned seven techniques in optimum design of pin jointed structures. First, a verification of the algorithms used to implement the techniques is carried out using a benchmark problem from the literature. Next, the techniques compiled in an unbiased coding platform are evaluated and compared in terms of their solution accuracies as well as convergence rates and reliabilities using four real size design examples formulated according to the design limitations imposed by ASD-AISC (Allowable Stress Design Code of American Institute of Steel Institution). The results reveal that simulated annealing and evolution strategies are the most powerful techniques, and harmony search and simple genetic algorithm methods can be characterized by slow convergence rates and unreliable search performance in large-scale problems.