In this study, a design procedure incorporating a genetic algorithm (GA) is developed for optimization of structures. The objective function considered is the total weight of the structure. The objective function is minimized subjected to displacement and strength requirements. In order to evaluate the design constraints, finite element analysis are performed either by using conventional finite element solvers (i.e. MSC/NASTRAN&reg / ) or by using in-house codes. The application of the algorithm is shown by a number of design examples. Several strategies for reproduction, mutation and crossover are tested. Several conclusions drawn from the research results are presented.

In this study, an optimization code has been developed based on genetic algorithms associated with the finite element modeling for the shape optimization of plane stress problems. In genetic algorithms, constraints are mostly handled by using the concept of penalty functions, which penalize infeasible solutions by reducing their fitness values in proportion to the degrees of constraint violation. In this study, An Improved GA Penalty Scheme is used. The proposed method gives information about unfeasible individual fitness as near as possible to the feasible region in the evaluation function. The objective function in this study is the area of the structure. The area is minimized considering the Von-Misses stress criteria. In order to minimize the objective function, one-point crossover with roulette-wheel selection approach is used. Optimum dimensions of four problems available in the literature have been solved by the code developed . The algorithm is tested using several strategies such as / different initial population number, different probability of mutation and crossover. The results are compared with the ones in literature and conclusions are driven accordingly.

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We use the elitist and extinction policies to improve the simple genetic algorithm in this study. We expect that the search technique can avoid falling into the local maximum due to the premature convergence, and the chance of finding the near-optimal parameter in the larger searching space could be obviously increased. The accelerometer is then taken as the sensor for output measurement, and the designed controller is implemented to actively suppress the vibration of the plain that is due to the excitation effect of the high-speed and precision positioning of the linear motor. From the computer simulations and the experimented results, it is obvious that the near-optimal controller designed by using genetic approach with elitist and extinction can improve the effect of vibration suppression; the settling time is also decrease. For the vibration suppressions of high-speed precision positioning problems, the results are satisfactory in the cases of short, middle and long distance.

In this paper, we prove that the simulation method for interval-censored data proposed by Fay (1999) is consistent in the sense that if we select a sample, then the estimate obtained from Turnbulls (1974) EM algorithm will converge to the true parameter when the sample size tends to infinity. We also propose a non-parametric rank test for interval-censored data to determine whether two populations come from the same distribution. Simulation result shows that the proposed test statistics performs pretty satisfactory.

With the rapid growth of load demand, the distribution system is becoming very complicated such that the operation efficiency and service quality are deteriorated during recent years. Engineers have to solve the problems by applying new technologies to enhance the efficiency of distribution system. In this thesis, an immune algorithm(IA) based on weighting selection as a decision maker is proposed to reach the desired switching operations such that transformer and feeder loading balance can be achieved. The IA antigen and antibody are equivalent to the objective and the feasible solution for a conventional optimization method. The concept of the information entropy is also introduced as a measure of diversity for the population to avoid falling into a local optimal solution. This algorithm prevents the possibility of stagnation in the iteration process and achieves the fast convergence for the global optimization. With the object-orient programming(OOP), this research project is to create the relationship of distribution element objects and encapsulation of data with all 22KV underground systems in Taichung district. The OOP does provide an effective tool for the management of distribution system database and the fault detection, isolation, and service restoration(FDIR) function of feeders and main transformers. According to the attributes of line switches, we can create the 22KV distribution system configuration with the topology processor. In order to calculate the current flows of line switches, this project will also execute the three phase load flow program with the customer information system(CIS), load survey, outage management information system(OMIS), and the data of all feeders and main transformers. In this thesis, the IA is used to solve the optimal switching problem by considering the customer load characteristics for the normal operation and the overload contingency of the distribution system. The efficiency of immune algorithm to solve the problem is verified by comparing to the computing time of the conventional binary integer programming for decision making of switching operation. A Taichung district distribution system is selected for computer simulation to demonstrate the effectiveness of the proposed methodology for solving the optimal switching operation of distribution system. The result of this thesis will be an important reference for distribution automation in Taiwan.

ABSTRACT In this dissertation, several adaptive control design schemes for a class of nonlinear systems are proposed. The first topic of the research is concerned with self-tuning PID controller design. The main problem of designing PID controller is how to determine the values of three control gains, i.e., proportional gain , integral gain , and derivative gain . We attempt to use the technique of adaptive control based on the Lyapunov approach to design the PID controller for some class of partially known nonlinear systems. Three PID control gains are adjusted on-line such that better output performance can be achieved. The stability of the closed-loop PID control systems is analyzed and guaranteed by introducing a supervisory control and a modified adaptation law with projection. Second, two kinds of adaptive neural control systems including the direct and indirect neural controls are considered by using simple single auto-tuning neuron. We will first propose a novel neuron called auto-tuning neuron and use it to take place of the roles of the traditional neural networks used in the direct and indirect adaptive neural control systems. This can greatly reduce the computational time and network complexities due to the simple configuration of the auto-tuning neuron. It is also easy for hardware implementation. Third, based on the idea borrowed from natural evolution, genetic algorithm can search for optimal or near-optimal solutions for an optimization problem over the search domain. An optimization technique of real-coded genetic algorithm is used to design the PID controller by minimizing the performance index of integrated absolute error. The improvements of our results over that using other methods are also illustrated. In the last part of each section, some computer simulation results will also be provided to illustrate our proposed methods.

A huge number of genomic information, including protein and DNA sequences, is generated by the human genome project. Deciphering these sequences and detecting local residue patterns of multiple sequences are very difficult. One of the ways to decipher these biological sequences is to detect local residue patterns from them. However, detecting unknown patterns from multiple sequences is still very difficult. In this thesis, we propose an algorithm, based on the Gibbs sampler method, for identifying local consensus patterns (motifs) in monomolecular sequences. We first designed an ACO (ant colony optimization) algorithm to find a good initial solution and a set of better candidate positions for revising the motif. Then the Gibbs sampler method is applied with these better candidate positions as the input. The required time for finding motifs using our algorithm is reduced drastically. It takes only 20 % of time of the Gibbs sampler method and it maintains the comparable quality.