This thesis investigates the effect of neighborhood structure on simulated annealing, a random search algorithm that has the ability to search through a sequence of local optimal solutions and return the globally optimal solution. Neighborhood sizes of one, two, three, four and N (equal to the size of the problem) decision variable alterations have been experimentally tested on the following problem types: Quadratic Assignment problem; Quadratic Selection problem; Stochastic Optimization problem; and Traveling Salesman problem. Empirical results indicate that a smaller neighborhood size is better. However, for some instances, a neighborhood size of one larger than the smallest defined neighborhood size performed best.

The data used for the construction of genome maps is imperfect, therefore the mapping of a physically linear structure must take place in a very uneven feature space. As the number of genes to be ordered grows, it appears to be impractical to use exhaustive search techniques to find the optimal mapping. In this paper we compare genetic algorithms and simulated annealing, two methods that are widely believed to be well-suited to non-smooth feature spaces, and find that the genetic algorithm approach yields superior results. Here we present performance profiles of comparable implementations of both genetic algorithms and simulated annealing. We have translated the problem to a form comparable to the shortest-path problem and found that the ability of a genetic algorithm to combine different partial solutions seems to be responsible for its superiority over the simulated annealing method. This is because in the genome mapping problem, as in the Traveling Salesman Problem, good solutions tend to be rather sparse and because optimal subtours tend to be components of nearly optimal tours. / Graduation date: 1994

The numbers of atoms in a protein molecule are large, from 103 to 104. If we try to solve the positions of all atoms in a protein molecule, we usually can¡¦t get the result due to tremendous degrees of freedom. Here, we use the uniform cylinders to replace the helixes found in most protein molecules, and reduce the degrees of freedom dramatically. We also adapt Su¡¦s method to avoid the ¡§X-ray phase problem¡¨. In this thesis, we simulate the small angle X-ray diffraction data of atoms which uniformly confined in cylinders, and then using trying cylinders to simulate the real cylinders. Our study shows that we can find the cylinders¡¦ positions quite successfully and efficiently. Our approach provides a promising way to find out the helix structure of proteins.

Abstract In radiation therapy, the beam angles and weightings are usually determined by a treatment planning planner. A uniform dose distribution depends on optimal radiation incident beam angle and individual beam weighting. In this study, simulated annealing with wavelet theory is adopted for solving the optimal beams¡¦ angles and weightings to accomplish a uniform dose distribution inside tumor lesion. Our method can increase the efficiency in solving the optimal angles and weightings in the process of simulated annealing.

Building a rule-based classification system for a training data set is an important research topic in the area of data mining, knowledge discovery and expert systems. Recently, the GA-based fuzzy approach is shown to be an effective way to design an efficient evolutionary fuzzy system. In this thesis a three layers genetic algorithm with Simulated Annealing for selecting a small number of fuzzy if-then rules to building a compact fuzzy classification system will be proposed. The rule selection problem with three objectives: (1) maximize the number of correctly classified patterns, (2) minimize the number of fuzzy if-then rules, and (3) minimize the number of required features. Genetic algorithms are applied to solve this problem. A set of fuzzy if-then rules is coded into a binary string and treated as an in-dividual in genetic algorithms. The fitness of each individual is specified by three ob-jectives in the combinatorial optimization problem. Simulated annealing (SA) is op-tionally cooperated with three layers genetic algorithm to effectively select some layer control genes. The performance of the proposed method for training data and test data is ex-amined by computer simulations on the iris data set and spiral data set, and comparing the performance with the existing approaches. It is shown empirically that the pro-posed method outperforms the existing methods in the design of optimal fuzzy sys-tems.

Thesis (M.S.)--West Virginia University, 2001. / Title from document title page. Document formatted into pages; contains x, 133 p. : ill. Includes abstract. Includes bibliographical references (p. 88-94).

Modern day wireless communication systems are constantly facing increasing bandwidth demands due to a growing consumer base. To cope up with it, they are required to have a better power vs performance from the RF devices. The amount of data being exchanged over wireless links has tremendously increased and simultaneously, there is a need to switch to portable RF devices and this has in turn forced the issue of low-power RF system design. Therefore, what we need is an RF transceiver that operates at high data rates and over adverse channels with a low power consumption. A major portion of the power is utilized by the RF front end of the wireless system. Many methods like controlled positive feedback, re-utilizing bias current, etc have been employed to reduce the power consumption of the RF front end. The most modern wireless systems adapt to the channel quality by adjusting the data transmission rates and by adjusting the output power of the RF Power Amplifier. However, each of these methods concentrates on working for the worst case channel and giving the highest data rate. What needs to be known is that the channel conditions are not always worst. Even for a normal channel, the system is going to utilize a lot of power and give the highest possible data rate which may or may not be necessary. And thus, for the most part, the system is going to use up more power than necessary. What we need instead, is a system which works nominally for a normal channel and exhaustively for a harsh channel condition. This requires the system to adapt to the channel conditions. Also another major factor causing fluctuations in the performance is the process variations. This calls for a channel-dependent dynamic transceiver with adequate power management and tuning. In our work, we try to devise a method to dynamically minimize the power considering the varying channel conditions and process variations. We first use companding to reduce the dynamic range of the signal so that it can be used on facilities with smaller dynamic range. This brings down the transmitted power. We also create multiple instances of the Power Amplifier to simulate process variations. After finding the optimum tuning knob settings for one instance of the PA, we try to use it to obtain the optimum settings for another instance. This requires the use of some heuristics and in our work, we have supplemented it with Simulated Annealing. Using SA, we can dynamically tune the power of a system for changing channel conditions and existing process variations. Towards the end, we have also proved that the slower the cooling rate of the experiment, the more elaborate the search space is and the more accurate the result is.

There are three implemented algorithms in this work: lessons permutation, lessons permutation with simulated annealing adjustment, lessons permutation using Bayesian approach theory to optimize SA parameters algorithms. Algorithms and graphical user interface are programmed with JSP which is based on Java object programming language. To evaluate schedule goodness algorithms are computing every penalty points which are given for some inconvenieces. User is able to define how much penalty points will be given if some inconveniece is satisfied. Also he is able to assign stochastic algorithm parameters. There was accomplished theory, where was observed using of simulated annealing and Bayesian approch methods in other stochastic algorithms and their different combination. There is a description of profiled school schedule optimization algorithm, which is based on SA searching methodology: searching for the optima through lower quality solutions, using temperature function which convergence, difference in quality. Algorythm which is using BA was created in case to improve SA searching methodology. User by changing systems temperature or annealing speed througth parameters can make big influence to SA behaviour. Passing parameters then using algorithm with BA meaner influence is made to behaviour because this method prognosticates, acording to him, better parameters with which SA should work effectively and changing them. Researches with three stochastic algorithms were made... [to full text]

An optimal stochastic multi-crop irrigation scheduling algorithm was developed which was able to incorporate complex farm system models, and constraints on daily and seasonal water use, with the objective of maximising farm profit. This scheduling method included a complex farm simulation model in the objective function, used decision variables to describe general management decisions, and used a custom heuristic method for optimisation. Existing optimal schedulers generally use stochastic dynamic programming which relies on time independence of all parameters except state variables, thereby requiring over-simplistic crop models. An alternative scheduling method was therefore proposed which allows for the inclusion of complex farm system models. Climate stochastic properties are modelled within the objective function through the simulation of several years of historical data. The decoupling of the optimiser from the objective function allows easy interchanging of farm model components. The custom heuristic method, definition of decision variables, and use of the Markov chain equation (relating an irrigation management strategy to mean water use) considerably increases optimisation efficiency. The custom heuristic method used simulated annealing with continuous variables. Two extensions to this method were the efficient incorporation of equality constraints and utilisation of population information. A case study comparison between the simulated annealing scheduler and scheduling using stochastic dynamic programming, using a simplistic crop model, showed that the two methods resulted in similar performance. This demonstrates the ability of the simulated annealing scheduler to produce close to optimal schedules. A second case study demonstrates the ability of the simulated annealing scheduler to incorporate complex farm system models by including the FarmWi$e model by CSIRO in the objective function. This case study indicates that under conditions of limited seasonal water, the simulated annealing scheduler increases pasture yield returns by an average of 10%, compared with scheduling irrigation using best management practice. Alternatively expressed, this corresponds to a 20-25% reduction in seasonal water use (given no change in yield return).

Thesis (Ph. D.)--University of Canterbury, 2007. / Typescript (photocopy). Accompanied by CD-ROM: Appendix 1: Electronic copy of source code and input data. Includes bibliographical references (leaves 173-179). Also available via the World Wide Web.