Numerical optimization techniques
Numerical optimization techniques
Fuzzy control and fuzzy systems
Fuzzy control and fuzzy systems
Automatic control systems (7th ed.)
Automatic control systems (7th ed.)
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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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) then the output of a properly designed controller approaches the solution of the equation f(x) - r = 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.