A NEW PROBABILISTIC ALGORITHM FOR SOLVING NONLINEAR EQUATIONS SYSTEMS
Thong Nguyen Huu · 2011
In this paper, we consider a class of optimization problems having the following characteristics: there exists a fixed number k (1≤k<n) which does not depend on the size n of the problem such that if we randomly change the value of k variables, it has the ability to find a new solution that is better than the current one, we call it Ok. We build a new set of probabilities for controlling changes of the values of the digits and build Probabilistic-Driven Search algorithm for solving single-objective optimization problems of the class Ok. We test this approach by implementing the algorithm on nonlinear equations systems, and we find very good results that are better than results of other authors