Artificial Intelligent Systems and Machine Learning

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Comparative Analysis of Genetic Algorithm and Ant Colony Optimization Metaheuristic Techniques


Affiliations
1 Department of Computer Engineering, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, India
     

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The Metaheuristic is a set of algorithmic concepts that can be used to define heuristic methods applicable to a wide set of different optimization problems such as routing, scheduling etc. This paper focuses on the comparative analysis of most successful methods of optimization techniques such as genetic algorithm (GA) and ant colony optimization (ACO) inspired by biological behavior. The main objective GA and ACO is to generate an optimal schedule so as to complete the tasks in minimum period of time as well as utilizing the resources in an efficient way.

Keywords

Meta-Heuristic Algorithms, Genetic Algorithms, Ant Colony Optimization.
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  • Comparative Analysis of Genetic Algorithm and Ant Colony Optimization Metaheuristic Techniques

Abstract Views: 256  |  PDF Views: 2

Authors

Geeta Jangra
Department of Computer Engineering, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, India
Rashmi Gupta
Department of Computer Engineering, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, India

Abstract


The Metaheuristic is a set of algorithmic concepts that can be used to define heuristic methods applicable to a wide set of different optimization problems such as routing, scheduling etc. This paper focuses on the comparative analysis of most successful methods of optimization techniques such as genetic algorithm (GA) and ant colony optimization (ACO) inspired by biological behavior. The main objective GA and ACO is to generate an optimal schedule so as to complete the tasks in minimum period of time as well as utilizing the resources in an efficient way.

Keywords


Meta-Heuristic Algorithms, Genetic Algorithms, Ant Colony Optimization.