Heuristic Kalman Algorithm

Abstract

In this chapter a new optimization method is presented, called the Heuristic Kalman Algorithm (HKA). This new algorithm is proposed as an alternative approach for solving continuous non-convex optimization problems. The principle of HKA is to consider explicitly the optimization problem as a measurement process intended 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. A significant advantage of HKA against other metaheuristics lies mainly in the small number of parameters which have to be set by the user. In addition, it is shown that HKA converges almost surely to a near-optimal solution. The efficiency of HKA is evaluated in detail through several non-convex 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 non-convex optimization problems, especially with regard to the computation time and the success ratio.KeywordsKalman FilterMeasurement ProcessSuccess RatioConditional Probability DensityStatic Output FeedbackThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.