Novel evolutionary methods in engineering optimization : towards robustness and efficiency
1University of Oulu, Faculty of Technology, Department of Process and Environmental Engineering
|Online Access:||PDF Full Text (PDF, 2.6 MB)|
|Persistent link:|| http://urn.fi/urn:isbn:9789514291579
|Publish Date:|| 2009-06-05
|Thesis type:||Doctoral Dissertation
|Defence Note:||Academic dissertation to be presented with the assent of the Faculty of Technology of the University of Oulu for public defence in Kuusamonsali (Auditorium YB210), Linnanmaa, on 17 June 2009, at 12 noon
Professor Hannu J. Koivisto
Doctor György Lipovszki
In industry there is a high demand for algorithms that can efficiently solve search problems. Evolutionary Computing (EC) belonging to a class of heuristics are proven to be well suited to solve search problems, especially optimization tasks. They arrived at that location because of their flexibility, scalability and robustness. However, despite their advantages and increasing popularity, there are numerous opened questions in this research area, many of them related to the design and tuning of the algorithms.
A neutral technique called Pseudo Redundancy and related concepts such as Updated Objective Grid (UOG) is proposed to tackle the mentioned problem making an evolutionary approach more suitable for “real world” applications while increasing its robustness and efficiency. The proposed UOG technique achieves neutral search by objective function transformation(s) resulting several advantageous features. (a) Simplifies the design of an evolutionary solver by giving population sizing principles and directions to choose the right selection operator. (b) The technique of updated objective grid is adaptive without introducing additional parameters, therefore no parameter tuning required for UOG to adjust it for different environments, introducing robustness. (c) The algorithm of UOG is simple and computationally cheap. (d) It boosts the performance of an evolutionary algorithm on high dimensional (constrained and unconstrained) problems.
The theoretical and experimental results from artificial test problems included in this thesis clearly show the potential of the proposed technique. In order to demonstrate the power of the introduced methods under “real” circumstances, the author additionally designed EAs and performed experiments on two industrial optimization tasks. Although, only one project is detailed in this thesis while the other is referred.
As the main outcome of this thesis, the author provided an evolutionary method to compute (optimal) daily water pump schedules for the water distribution network of Sopron, Hungary. The algorithm is currently working in industry.
Acta Universitatis Ouluensis. C, Technica
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