Statistical Approach for Selecting Elite Ants
G. S. Raghavendra, N. Prasanna Kumar
TL;DR
This paper introduces a statistical method for dynamically selecting elite ants in Ant Colony Optimization algorithms to improve solution quality for combinatorial problems.
Contribution
It proposes new statistical mechanisms for elite ant selection and evaluates their performance within ACO frameworks.
Findings
New mechanisms improve solution quality
Dynamic selection adapts better to problem instances
Performance varies with different statistical tools
Abstract
Applications of ACO algorithms to obtain better solutions for combinatorial optimization problems have become very popular in recent years. In ACO algorithms, group of agents repeatedly perform well defined actions and collaborate with other ants in order to accomplish the defined task. In this paper, we introduce new mechanisms for selecting the Elite ants dynamically based on simple statistical tools. We also investigate the performance of newly proposed mechanisms.
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Constraint Satisfaction and Optimization · Scheduling and Optimization Algorithms