Optimization of capillary tube stacking - Liu, Qinghua (2015)
Genetic algorithm is an evolutionary numerical method, which helps solving optimization problems in an efficient way. Genetic algorithm is based on the belief that “the fittest survives”, a theory proposed by Darwin in his evolution theory. A typical genetic algorithm maps the physical parameters into biological entities, and imitates the natural selection process. A solution candidate is mapped into a set of single or multiple binary bit strings, called chromosomes. A solution candidate is an individual in its generation. An individual’s fitness is measured by a fitness function, and the fitter individuals will be selected through the selection operator to participate in the reproduction of the next generation individual. Through a well - designed selection and crossover method, the fittest individuals and “genes” will be preserved, maintained and generated, and eventually, all the individuals will converge towards the fittest value. One advantage of genetic algorithm is its generic nature, which enables it to solve all types of optimization problems: as long as a candidate solution can be modeled as a set of chromosomes, its optimized solution can be found. Currently in photonic crystal fibre design, there is lack of an automated method to help the scientists and researcher to decide the sizes and positions the filler capillaries, whose functionality is to provide physical structural support for the capillaries. Currently, designers and researchers have to manually produce the design graphs; this means the outcomes are not precise and often lead to the failure of production. This thesis is aimed to analyze the crystal fibre structure, and utilize genetic algorithm, to design and implement a computer program to assist the designers in their stacking process. The program is aimed to be able to calculate the size and the position of the filler capillaries, and thus, provide an optimized solution to the photonic crystal fibre stacking problem.