Intelligent Inversion Algorithms And Applications

In order to improve premature convergence and reduce calculation cost of inversion, this dissertation is devoted to studying intelligent inversion algorithms based on some novel intelligent algorithms considering some inverse problems existing in civil engineering.Based on the extensive investigation of the literature, research situation of inverse problems in geotechnical engineering is summarized. Inversion algorithms existing in different subjects are classified in detail. Among these algorithms, intelligent inversion algorithms are one of main research directions in the study of inverse problems. Premature convergence, enormous calculation cost and reliability of solution are realized the main existing problems in intelligent inversion algorithms. The significance of this dissertation is expounded and the research methods are determined.In order to improve premature convergence existing in current intelligent inversion algorithms, Ant Colony Algorithm, a new simulating evolutionary algorithm proposed recently for solving hard combinatorial optimization problems, is introduced and modified for parameter inversion in civil engineering. For the purpose applying Ant Colony Algorithm to parameter inversion, the search space of parameters to be inversed is discretized firstly so that inverse problem is transformed into a combinatorial optimization problem. And then Ant Colony Algorithm is modified by replacing tour length and visibility in it with objective function value and standard deviation of objective function value respectively. The results of a numerical simulation show that the modified Ant Colony Algorithm can improve premature efficiently.Particle Swarm Optimization, a new simulating evolutionary algorithm proposed recently, is suggested to reduce the calculation cost of solving inverse problems. The principle and the main characteristics of Particle Swarm Optimization are introduced, and the advantages and weaknesses of this novel optimizer are summarized. A theoretical analysis on the convergence behavior of it is carried out and the value ranges of parameters guarantee the convergence of the algorithm is given. Particle Swarm Optimization is modified so that it can solve inverse problems more efficiently. The auto-adaptive ability of origin algorithm to the constraint conditions is increased and an expression of dynamic inertia weight is proposed. Numerical results show that Particle Swarm Optimization can quickly locate the optimum with a small population size and it can reduce calculation cost of solving inverse problems efficiently.For hybrid algorithms can take advantages of sub-algorithms in it, hybrid optimization strategies are suggested to solve inverse problems. Two types of hybrid algorithms based on intelligent algorithms are proposed. Firstly, simplex method is chosen and combined with Particle Swarm Optimization to improve local searching ability of Particle Swarm Optimization. Based on the compare of different types of hybrid frameworks for hybrid algorithms, the inlaid framework is chosen to combine these two algorithms. And a hybrid probability function depended on time and standard deviation of objective function value ispresented to improve the efficiency of solutions. Secondly, immune selection mechanism is chosen and combined with Particle Swarm Optimization to reduce calculation cost. Both of these two hybrid algorithms were applied to parameter inversion in civil engineering. Numerical results show that hybrid algorithms are of strong ability to obtain global minima, and its performances are superior to those of single methods.In order to improve the reliability of inverse result, the numerical approximation method is adopted to analysis reasons for the distortion of inverse result. And local model and global model for parameter inversion are defined. According to the balance equation of finite element method, the conditions that must be satisfied to avoid the nonuniqueness of inverse result are discussed. According to these conditions, two kinds of method are proposed and applied to parameters inversion. One is virtual displacement method. It can compensate the deficiency of observed data to improve the reliability of inverse result. Another is sub-region method. For only part of analysis object is calculated in this method, it can reduce calculation cost. The results of an example show that the virtual displacement method is of strong ability to obtain global minima, and it can improve the reliability of inverse result efficiently.The main contributions are summarized and further works are suggested at the end of this dissertation,...