GREY WOLF OPTIMIZATION METHOD ENHANCED WITH CLUSTERING TECHNIQUE FOR SOLVING NONLINEAR EQUATION SYSTEMS AND SEVERAL ENGINEERING PROBLEMS
Metaheuristic methods have been widely used to solve various optimization problems. One of these metaheuristic methods is Grey Wolf Optimization, first proposed by Mirjalili in 2014. This method draws inspiration from social hierarchies and the hunting mechanism of gray wolves (Mirjalili dkk., 20...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/78058 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Metaheuristic methods have been widely used to solve various optimization
problems. One of these metaheuristic methods is Grey Wolf Optimization, first
proposed by Mirjalili in 2014. This method draws inspiration from social hierarchies
and the hunting mechanism of gray wolves (Mirjalili dkk., 2014). In this
final project, the author will improvise upon this method to make it applicable for
solving non-linear equation systems, Diophantine equations and systems, multimodal
functions, and several real problems in the field of engineering. The
author will also integrate the GWO method with clustering techniques to divide a
domain based on regions that are believed to contain optimal solutions (Sidarto and
Kania, 2015). The Sobol sequence is employed to generate the initial population,
ensuring a more uniform distribution of initial values. Experimental results demonstrate
that the GWO algorithm yields favorable outcomes in searching for optimal
values and exhibits a sufficiently fast convergence rate. |
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