| Name: | Description: | Size: | Format: | |
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| 15.12 MB | Adobe PDF |
Advisor(s)
Abstract(s)
Sectorization problems have significant challenges arising
from the many objectives that must be optimised simultaneously. Several
methods exist to deal with these many-objective optimisation problems,
but each has its limitations. This paper analyses an application of Preference
Inspired Co-Evolutionary Algorithms, with goal vectors (PICEA-g)
to sectorization problems. The method is tested on instances of different
size difficulty levels and various configurations for mutation rate and population
number. The main purpose is to find the best configuration for
PICEA-g to solve sectorization problems. Performancemetrics are used to
evaluate these configurations regarding the solutions’ spread, convergence,
and diversity in the solution space. Several test trials showed that big and
medium-sized instances perform better with low mutation rates and large
population sizes. The opposite is valid for the small size instances.
Description
Keywords
Sectorization Problems Co-evolutionary algorithms Many objectives optimization