Simulated annealing: theory and applications
Simulated annealing: theory and applications
Modified iterated simulated annealing algorithm for structural synthesis
Advances in Engineering Software - design optimization
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Efficiency improvement of simulated annealing in optimal structural designs
Advances in Engineering Software - Engineering computational technology
Layout optimisation of trusses using simulated annealing
Advances in Engineering Software - Engineering computational technology
Ant Colony Optimization
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Optimum geometry design of nonlinear braced domes using genetic algorithm
Computers and Structures
A heuristic particle swarm optimizer for optimization of pin connected structures
Computers and Structures
Stacking sequence design of a composite wing under a random gust using a genetic algorithm
Computers and Structures
Particle swarm approach for structural design optimization
Computers and Structures
Size optimization of space trusses using Big Bang-Big Crunch algorithm
Computers and Structures
Heuristic optimization of RC bridge piers with rectangular hollow sections
Computers and Structures
Geometry and topology optimization of geodesic domes using charged system search
Structural and Multidisciplinary Optimization
Improved harmony search algorithms for sizing optimization of truss structures
Computers and Structures
Cellular growth algorithms for shape design of truss structures
Computers and Structures
Fully Stressed Design Evolution Strategy for Shape and Size Optimization of Truss Structures
Computers and Structures
Krill herd algorithm for optimum design of truss structures
International Journal of Bio-Inspired Computation
Chaotic swarming of particles: A new method for size optimization of truss structures
Advances in Engineering Software
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This paper presents an optimization algorithm based on Simulated Annealing. The algorithm - denoted as CMLPSA (Corrected Multi-Level &Multi-Point Simulated Annealing) - implements an advanced search mechanism where each candidate design is selected from a population of trial points randomly generated. Therefore, CMLPSA is in principle similar to meta-heuristic algorithms dealing with a pool/population of designs rather than with a single trial point such as it is usually done in classical simulated annealing. The multi-point strategy is adopted for both feasible and infeasible intermediate designs. In the former case, perturbations given to optimization variables are forced to follow the current rate of change exhibited by the cost function. In the latter case, 4th order approximate line search is performed in the neighbourhood of each feasible trial point generated in the current annealing cycle. Furthermore, CMLPSA includes a multi-level annealing strategy where trial points are generated by perturbing all design variables simultaneously (global level) or one by one (local level). Global or local search is performed basing on the current trend seen in the optimization process. CMLPSA is tested in six structural optimization problems where the objective is to minimize the weight of bar trusses - with up to 200 elements - subject to constraints on nodal displacements, member stresses and critical buckling loads. Test cases include both sizing and lay-out optimization variables. The computationally most expensive problem has 200 design variables and 3500 optimization constraints. CMLPSA is compared with other state-of-the-art SA algorithms and advanced global optimization methods like Heuristic Particle Swarm Optimization (HPSO) and Harmony Search (HS) recently presented in literature. Numerical results clearly demonstrate efficiency and robustness of CMLPSA. In particular, CMLPSA found better designs than the other SA-based algorithms and converged much more quickly to the optimum than HPSO and HS. Furthermore, CMLPSA is insensitive to initial design.