In this study, a new global optimization method that uses a closed loop control system is proposed. If a plant, in a feedback control system with a reference input r, is replaced by the objective function f ((x) under bar) then the output of a properly designed controller approaches the solution of the equation f((x) under bar) - (r) under bar = 0 at the steady state. An algorithm is then designed such that the reference point and the objective function representing the plant are continuously changed within the control loop. This change is done in accordance with the result of the steady-state controller output. This algorithm can find the global optimum point in a bounded feasible region. Even though the new approach is applicable to the optimization of single and multivariable non-linear objective functions,- only the results related to some test functions with single variable are presented. The results of the new algorithm are compared with some well-known global optimization algorithms. (C) 2002 Elsevier Science Ltd. All rights reserved.