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• [P520] Constrained Optimization using the Lagrangian Method and the Improved Discrete Gradient Chaos Model
  Author(s): Takashi Okamoto, Hironori Hirata
  
  

  Abstract
  In this study, we propose a new chaotic global optimization method using the Lagrangian method to solve a nonlinear constrained optimization problem. First, we explain convergent property of the first order method regarding convexity of the Lagrangian function with respect to decision variables in terms of the linear stability theory. Further, we propose a new optimization method in which convergent property of the first order method is improved by two techniques. Then, we apply the multipoint type chaotic optimization method so that the global search is implemented to find feasible global minima. We then confirm effectiveness of the proposed method through the applications to the coil spring design problem and benchmark problems used in special session on constrained real parameter optimization in CEC2006.

  
   

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