The Application Of Computational Intelligence In Control, Optimization, And Decision

Having developed for half an century, the conventional optimization algorithms which are based on the Operational Research Theory and some Mathematical Programming tools come into mature. Such algorithms have been widely used in many fields due to their high efficiency and robustness. However, they usually require the optimized functions to be continuous even high order differentiable. What is more, they heavily rely on the initial position of search, without guarantee of the global solutions. When coming to difficult problems especially those exist in the Uncertain System Optimization, people sometimes resort to Computational Intelligence.The object of this thesis is the application of Computational Intelligence in Optimization, not only the single level optimization, but also the multi level optimization. Both the efficiency and the stability of algorithms are considered. For general nonlinear optimization with constraints, a neural network model and a revised PSO algorithm are proposed. For minimax two level optimization problem, neural network and genetic algorithm solutions are provided, respectively. After that, the minimax optimization is applied into the uncertain system analysis. Through minimax algorithm the regret index could be calculated, then the Interval Programming is translated into equivalent Multiobjective Programming. Another application example of minimax optimization is the robust PID controller design. By this design measurement the control quality is guaranteed even in the worst working conditions.The main contributions of this thesis are listed as following:1. The history and the current research progress of Computational Intelligence is synthesized, especially those algorithms applied in the optimization with interval number parameters, an important branch of the uncertain system.2. A neural network solver for the Augmented Lagrange multiplier (ALM) method is provided, which has a wide application in the constrained nonlinear optimization propositions. A dynamic approach for the minimization subproblem in ALM method is discussed, and then a neural network iterative algorithm is proposed for general constrained nonlinear optimization.3. A PSO with an increasing inertia weight, distinct from a widely used PSO with a decreasing inertia weight, is proposed for the single level optimization problems. Far from drawing conclusions from sole empirical studies or rule of thumb, this algorithm is derived from particle trajectory study and convergence analysis. Four standard test functions with asymmetric initial range settings are used to confirm the validity of the PSO with an increasing inertia weight. From the experiments, it is clear that a PSO with an increasing inertia weight outperforms the one with a decreasing inertia weight, both in convergent speed and solution precision, with no additional computing load compared with the PSO with a decreasing inertia weight.4. The minimax problem is a significant topic in signal process and process control, which is relevant to robustness, parameters uncertainty, and signal noise etc. However, efficient algorithms are scarce, especially those for general minimax problem with nonlinear equality and inequality constraints. A novel neural network for general minimax problem has been constructed based on a penalty function approach. The only request on objective function and constraint functions is that they should be first-order differentiable. A Lyapunov function is established for the global stability analysis. The SGA (Simplex-Genetic Algorithm), an improved algorithm of Genetic Algorithm for solving Stackelberg-Nash Equilibrium, is also proposed for the minimax optimization.5. For the uncertainty optimization with interval coefficients in the objective function, a robust optimization framework is proposed, in which the concept of"regret" is incorporated. This framework is inspired by the methodology of "Wuli-Shili-Renli" [26] raised by J. Gu. Through this method an uncertainty optimization problem may be transferred i...