Date of Award

2007

Degree Type

Thesis

Degree Name

Master of Applied Science (MASc)

Department

Chemical Engineering

First Advisor

Simant R Upreti

Abstract

Genetic algorithm (GA) is a promising means to solve engineering optimization problems. GA is able to perform the global search with minimal simplifying assumptions about the problem as well as the corresponding decision space. GA face problems like premature convergence and slow convergence due to decreasing population diversity. To surmount this problem, we have developed a new approach of optimal genotypic feedback (OGF). This approach generates binary building blocks of random size from the optimal solution after each generation. The blocks are then inserted in the subsequent generation. This new apporach is successfully tested on number of nonlinear, multimodal and non-continuous optimization problems. The results demonstrate that the approach efficiently searches good quality solutions.

In the next step, OGF is amalgamated with hybrid GA (HGA). The resulting new HGA is applied on six optimization problems involving characterization parameters of pulp chest and minimum variance control. Comparisons with the old HGA indicate the equivalence of OGF with gradient search. Furthermore, the new HGA is observed to yield results in less number of objective function evaluations.


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