A Chaotic Implementation of Two Meta-Heuristic Algorithms
Document Type
Poster
Campus where you would like to present
Ellensburg
Event Website
https://digitalcommons.cwu.edu/source
Start Date
15-5-2019
End Date
15-5-2019
Abstract
Within the field of Computer Science, there exists a category called Optimization. Optimization can be viewed as an algorithmic design problem where the objective is to be more efficient in terms of resources. One of the key facets of algorithm design is to reduce computational time. Modern optimization algorithms allows us to meet this objective. This research compares two modern optimization algorithms of the Firefly Algorithm proposed by Dr. Xin-She Yang and the Grasshopper algorithm developed by Dr. Seyedali Mirjalili. Both of these algorithms are meta-heuristics, which means they are based off of the real-life behavior of living creatures. The Firefly Algorithm is based off of the swarming behavior of fireflies while the Grasshopper Optimization Algorithm is based off of the swarming behavior of grasshoppers. These algorithms are used to find the global optima of a given mathematical function. The global optima in this case is either the minimum or maximum value output from a function. These problems can become intractable when the dimensionality of the problems increases. These multi-dimensional calculations are very resource heavy as they use up a lot of time and memory. The objective is to reduce the amount of resources these algorithms use through optimization. The research includes a significant amount of numerical experimentations and its analysis on standard benchmark suites from literature. Additionally, chaotic randomization will be implemented to improve the algorithms.
Winner, Outstanding Poster Presentation, College of the Sciences.
Recommended Citation
Halaapiapi, Leni, "A Chaotic Implementation of Two Meta-Heuristic Algorithms" (2019). Symposium Of University Research and Creative Expression (SOURCE). 139.
https://digitalcommons.cwu.edu/source/2019/Posters/139
Department/Program
Computer Sciences
A Chaotic Implementation of Two Meta-Heuristic Algorithms
Ellensburg
Within the field of Computer Science, there exists a category called Optimization. Optimization can be viewed as an algorithmic design problem where the objective is to be more efficient in terms of resources. One of the key facets of algorithm design is to reduce computational time. Modern optimization algorithms allows us to meet this objective. This research compares two modern optimization algorithms of the Firefly Algorithm proposed by Dr. Xin-She Yang and the Grasshopper algorithm developed by Dr. Seyedali Mirjalili. Both of these algorithms are meta-heuristics, which means they are based off of the real-life behavior of living creatures. The Firefly Algorithm is based off of the swarming behavior of fireflies while the Grasshopper Optimization Algorithm is based off of the swarming behavior of grasshoppers. These algorithms are used to find the global optima of a given mathematical function. The global optima in this case is either the minimum or maximum value output from a function. These problems can become intractable when the dimensionality of the problems increases. These multi-dimensional calculations are very resource heavy as they use up a lot of time and memory. The objective is to reduce the amount of resources these algorithms use through optimization. The research includes a significant amount of numerical experimentations and its analysis on standard benchmark suites from literature. Additionally, chaotic randomization will be implemented to improve the algorithms.
Winner, Outstanding Poster Presentation, College of the Sciences.
https://digitalcommons.cwu.edu/source/2019/Posters/139
Faculty Mentor(s)
Donald Davendra