whole_KelarevaGalinaVladislavovna2003.pdf (20.55 MB)
Development and applications of multi-layered genetic algorithms to multi-dimensional optimisation problems
thesisposted on 2023-05-26, 16:40 authored by Kelareva, Galina Vladislavovna
Genetic algorithms represent a global optimisation method, imitating the principles of natural evolution: selection and survival of the fittest. Genetic algorithms operate on a randomly initialised population of potential solutions to a problem. The solutions develop by passing valuable genetic information to succeeding generations. Genetic algorithms are known as a robust technique suitable for a variety of optimisation problems. However, when applied to complex combinatorial problems with multiple parameters, conventional genetic algorithms are usually slow and ineffective due to the large search space. This thesis proposes a novel approach to the development of a genetic algorithm and applies this approach to a maintenance scheduling problem in a power generation system. Problem specific knowledge is utilised to divide the problem into several layers, with each layer representing a part of the initial problem. Solutions are progressively developed, with each layer algorithm finding partial solutions that satisfy specified criteria. These partial solutions are then used as building blocks in the next layer, to progressively build up complete solutions. The resulting multi-layered genetic algorithm is able to concentrate its search efforts in areas where good quality solutions are likely to be present, therefore producing better results than traditional genetic algorithms. Further developments of the multi-layered genetic algorithm are also suggested in this thesis. The algorithm is combined with a local search method, and heuristic rules are used for initialisation of the population. The combined method results in an effective and fast exploration of the problem's search space and is suitable for a variety of optimisation problems. The proposed algorithm is implemented using MATLAB programming language and tested on a real power generation system. A number of implementation issues, such as specific chromosome structure and a varying generation gap; interchangeable solutions and gene convergence; weeding out duplicates from the population and reducing the search space without losing the quality of representing the problem domain, are all discussed. Specifics of a local search method and its representation are also examined. Special attention is paid to developing efficient evaluation and neighbourhood exploration procedures.
Rights statementCopyright 2003 the Author - The University is continuing to endeavour to trace the copyright owner(s) and in the meantime this item has been reproduced here in good faith. We would be pleased to hear from the copyright owner(s). Thesis (Ph.D.)--University of Tasmania, 2003. Includes bibliographical references