Browsing by Author "Dr. John W. Baugh Jr., Committee Co-Chair"

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    A Distributed Simulated Annealing framework for Engineering Optimization
    (2003-09-11) Pabolu, Sivakumar V; Dr. G. (Kumar) Mahinthakumar, Committee Co-Chair; Dr. John W. Baugh Jr., Committee Co-Chair; Dr. Abhinav Gupta, Committee Member
    Engineering optimization problems are known to be difficult to solve using mathematical programming techniques because of large search spaces, complex objective and constraint functions, and, in many cases, their combinatorial nature. Simulated annealing is a well known heuristic optimization technique that has been used to solve a number of problems in discrete, non-differential, and combinatorial optimization and hence is suitable for solving such engineering optimization problems. However, computationally intensive problems are frequently encountered in the field of engineering optimization, in which case the use of simulated annealing can be prohibitively time consuming. The objective of this thesis is to develop an object oriented framework that implements a distributed simulated annealing algorithm, which can be easily extended to solve computationally intensive engineering optimization problems. A distributed simulated annealing algorithm (DSA Algorithm) was developed and incorporated into a distributed simulated annealing framework called the DSA Framework. The framework defines interfaces, through which optimization problems can be modeled, utilizing a distributed computing framework, Vitri, to engage multiple desktop computers in a collective effort to solve problems. The framework was used to solve a 40 variable knapsack problem as a benchmark problem to analyze the performance of the algorithm. The framework was also used to optimize support locations in a piping system subject to seismic loads. The DSA framework proves to be an efficient, fairly scalable tool that shows consistent reduction in execution time with increasing number of servers, thus proving to be a valuable tool in solving computationally intensive engineering optimization problems.