Theoretical and Practical Convergence of a Self-Adaptive Penalty Algorithm for Constrained Global Optimization.
M. Fernanda P. CostaRogério B. FranciscoAna Maria A. C. RochaEdite M. G. P. FernandesPublished in: J. Optim. Theory Appl. (2017)
Keyphrases
- constrained global optimization
- iterative algorithms
- detection algorithm
- learning algorithm
- convergence rate
- theoretical analysis
- high accuracy
- experimental evaluation
- dynamic programming
- computational cost
- improved algorithm
- simulated annealing
- objective function
- classification algorithm
- preprocessing
- computational complexity
- neural network
- similarity measure
- recognition algorithm
- cost function
- optimization algorithm
- computationally efficient
- linear programming
- significant improvement
- np hard
- tree structure
- global optimization
- particle swarm optimization
- convergence speed
- search space
- computationally demanding
- convergence property