A Chaotic Implementation of Two Meta-Heuristic Algorithms

Presenter Information

Leni Halaapiapi

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.

Faculty Mentor(s)

Donald Davendra

Department/Program

Computer Sciences

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May 15th, 12:00 AM May 15th, 12:00 AM

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