Lagrange multipliers and optimality
SIAM Review
Enhanced simulated annealing for globally minimizing functions of many-continuous variables
ACM Transactions on Mathematical Software (TOMS)
Use of a self-adaptive penalty approach for engineering optimization problems
Computers in Industry
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
Convex Optimization
Evolutionary Computation: Toward a New Philosophy of Machine Intelligence (IEEE Press Series on Computational Intelligence)
An effective co-evolutionary particle swarm optimization for constrained engineering design problems
Engineering Applications of Artificial Intelligence
Design of mixed H2/H∞ control systems using algorithms inspired by the immune system
Information Sciences: an International Journal
Brief paper: Robust PID controller tuning based on the constrained particle swarm optimization
Automatica (Journal of IFAC)
A tabu search approach for the minimum sum-of-squares clustering problem
Information Sciences: an International Journal
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An improved vector particle swarm optimization for constrained optimization problems
Information Sciences: an International Journal
Tabu search based multi-watermarks embedding algorithm with multiple description coding
Information Sciences: an International Journal
Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions
Information Sciences: an International Journal
Function optimisation by learning automata
Information Sciences: an International Journal
Hi-index | 0.07 |
In this work a new optimization method, called the heuristic Kalman algorithm (HKA), is presented. This new algorithm is proposed as an alternative approach for solving continuous, non-convex optimization problems. The principle of HKA is to explicitly consider the optimization problem as a measurement process designed to give an estimate of the optimum. A specific procedure, based on the Kalman estimator, was developed to improve the quality of the estimate obtained through the measurement process. The main advantage of HKA, compared to other metaheuristics, lies in the small number of parameters that need to be set by the user. Further, it is shown that HKA converges almost surely to a near-optimal solution. The efficiency of HKA was evaluated in detail using several non-convex test problems, both in the unconstrained and constrained cases. The results were then compared to those obtained via other metaheuristics. The numerical experiments show that HKA is a promising approach for solving non-convex optimization problems, particularly in terms of computation time and success ratio.