title: ACOustic: A Nature-Inspired Exploration Indicator for Ant Colony Optimization.
abstract: A statistical machine learning indicator, ACOustic, is proposed to evaluate the exploration behavior in the iterations of ant colony optimization algorithms. This idea is inspired by the behavior of some parasites in their mimicry to the queens' acoustics of their ant hosts. The parasites' reaction results from their ability to indicate the state of penetration. The proposed indicator solves the problem of robustness that results from the difference of magnitudes in the distance's matrix, especially when combinatorial optimization problems with rugged fitness landscape are applied. The performance of the proposed indicator is evaluated against the existing indicators in six variants of ant colony optimization algorithms. Instances for travelling salesman problem and quadratic assignment problem are used in the experimental evaluation. The analytical results showed that the proposed indicator is more informative and more robust.
authors.affiliation: Computer Science Department, University of Babylon, Babylon, Iraq.
authors.forename: Rafid
authors.initials: R
authors.lastname: Sagban
authors.affiliation: School of Computing, College of Arts and Sciences, Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia.
authors.forename: Ku Ruhana
authors.initials: KR
authors.lastname: Ku-Mahamud
authors.affiliation: School of Computing, College of Arts and Sciences, Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia.
authors.forename: Muhamad Shahbani
authors.initials: MS
authors.lastname: Abu Bakar
mesh_terms: D000465:Algorithms; D000818:Animals; D001000:Ants; D001522:Behavior, Animal; D000069550:Machine Learning; D015233:Models, Statistical