ACM Transactions on Mathematical Software (TOMS)
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Lagrange multipliers and optimality
SIAM Review
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Enhanced simulated annealing for globally minimizing functions of many-continuous variables
ACM Transactions on Mathematical Software (TOMS)
Outline for a Logical Theory of Adaptive Systems
Journal of the ACM (JACM)
Use of a self-adaptive penalty approach for engineering optimization problems
Computers in Industry
Learning mixtures of arbitrary gaussians
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Genetic AlgorithmsNumerical Optimizationand Constraints
Proceedings of the 6th International Conference on Genetic Algorithms
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Convex Optimization
An effective co-evolutionary particle swarm optimization for constrained engineering design problems
Engineering Applications of Artificial Intelligence
Extending particle swarm optimisers with self-organized criticality
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Brief paper: Robust PID controller tuning based on the constrained particle swarm optimization
Automatica (Journal of IFAC)
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 0.01 |
The main objective of this paper is to present a new optimization approach, which we call heuristic Kalman algorithm (HKA). We propose it as a viable approach for solving continuous nonconvex optimization problems. The principle of the proposed approach is to consider explicitly the optimization problem as a measurement process designed to produce an estimate of the optimum. A specific procedure, based on the Kalman method, was developed to improve the quality of the estimate obtained through the measurement process. The efficiency of HKA is evaluated in detail through several nonconvex test problems, both in the unconstrained and constrained cases. The results are then compared to those obtained via other metaheuristics. These various numerical experiments show that the HKA has very interesting potentialities for solving nonconvex optimization problems, notably concerning the computation time and the success ratio.