A new parameter adaptation method for Genetic Algorithms and Ant Colony Optimization algorithms

This Ant Colony Optimization algorithms and Genetic Algorithms are actively used in controller design, robotic path planning, design automation, biomedical imaging, data mining, and distribution network planning. This paper introduces a genetic algorithm implementation, an ant colony optimization al...

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Veröffentlicht in:IEEE International Conference on Electro Information Technology S. 0668 - 0673
Hauptverfasser: Byerly, Adam, Uskov, Alexander
Format: Tagungsbericht Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.05.2016
Schlagworte:
Algorithm design and analysis
Algorithms
Ant colony optimization
Automation
Biological cells
Combinatorial
Design engineering
Design optimization
Electronics
Genetic algorithms
Heuristic
Parameter Adaptation
Parameters
Sociology
Stagnation
Statistics
Traveling Salesman Problem
Urban areas
ISSN:2154-0373
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Zusammenfassung:This Ant Colony Optimization algorithms and Genetic Algorithms are actively used in controller design, robotic path planning, design automation, biomedical imaging, data mining, and distribution network planning. This paper introduces a genetic algorithm implementation, an ant colony optimization algorithm implementation, and a method of adapting the parameters for the algorithms during the course of their execution whenever they cease producing better solutions. Additionally, it presents the results of experiments performed with and without the method applied. The obtained research outcomes clearly show that the method has the great potential to improve the solutions arrived at in both types of nature inspired algorithms, though the greater improvement is achieved whenever an algorithm tends to stagnate further from the theoretical optimum as happened with the genetic algorithm as compared to with the ant colony optimization algorithm.
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SourceType-Conference Papers & Proceedings-2
ISSN:2154-0373
DOI:10.1109/EIT.2016.7535319