Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Journal of Computational Physics
Improved genetic operators for structural engineering optimization
Advances in Engineering Software
Efficient generation of the binary reflected gray code and its applications
Communications of the ACM
Advances in Engineering Software
Design of reinforced concrete bridge frames by heuristic optimization
Advances in Engineering Software
Optimum detailed design of reinforced concrete continuous beams using Genetic Algorithms
Computers and Structures
Heuristic optimization of RC bridge piers with rectangular hollow sections
Computers and Structures
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hi-index | 0.00 |
This paper aims at the automatic design and cost minimization of reinforced concrete vaults used in road construction. This paper presents three heuristic optimization methods: the multi-start global best descent local search (MGB), the meta-simulated annealing (SA) and the meta-threshold acceptance (TA). Penalty functions are used for unfeasible solutions. The structure is defined by 49 discrete design variables and the objective function is the cost of the structure. All methods are applied to a vault of 12.40m of horizontal free span, 3.00m of vertical height of the lateral walls and 1.00m of earth cover. This paper presents two original moves of neighborhood search and an algorithm for the calibration of SA-TA algorithms. The MGB algorithm appears to be more efficient than the SA and the TA algorithms in terms of mean results. However, the SA outperforms MGB and TA in terms of best results. The optimization method indicates savings of about 10% with respect to a traditional design.