Nonmonotone hybrid tabu search for inequalities and equalities: an experimental study

Abstract

The main goal of this paper is to analyze the behavior of nonmonotone hybrid tabu search approaches when solving systems of nonlinear inequalities and equalities through the global optimization of an appropriate merit function. The algorithm combines global and local searches and uses a nonmonotone reduction of the merit function to choose the local search. Relaxing the condition aims to call the local search more often and reduces the overall computational effort.

Combined mutation differential evolution to solve systems of nonlinear equations

“…Other stochastic approaches have been used to compute a root of the system (1) by solving the global optimization problem (2). A tabu search based framework has been implemented together with a local search strategy to enhance the search about a promising region and improve the quality of the solution [6,7]. In [8], a new technique for solving systems of nonlinear equations reshaping the system as a multiobjective optimization problem is proposed.…”