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Interacted Multiple Ant Colonies Optimization Framework: an Experimental Study of the Evaluation
and the Exploration Techniques to Control the Search Stagnation
Alaa Aljanaby, Ku Ruhana Ku-Mahamud, Norita Md. Norwawi
Interacted Multiple Ant Colonies Optimization Framework: an
Experimental Study of the Evaluation and the Exploration Techniques to
Control the Search Stagnation
Alaa Aljanaby 1, Ku Ruhana Ku-Mahamud 2, Norita Md. Norwawi 3
1College of Arts and Sciences, University of Nizwa, Oman
2 College of Arts and Sciences, Universiti Utara Malaysia, Malaysia
3Faculty of Science and Technology, Universiti Sains Islam Malaysia, Malaysia
alaa@.om, ruhana@.my, norita@.my
doi: 10.4156/ijact.vol2.issue1.7
Abstract
Search stagnation is a serious problem that all Ant Colony Optimization (ACO) algorithms suffer
from regardless of their application domain. The framework of Interacted Multiple Ant Colonies
Optimization (IMACO) is a recent proposition. It divides the ants’ population into several colonies and
employs certain techniques to organize the work of these colonies. This paper proposes new effective
evaluation and exploration techniques for IMACO and experimentally tests the stagnation behavior of
IMACO. The performance of IMACO was demonstrated by comparing it with the best performing ant
algorithms like Ant Colony System (ACS) and Max-Min Ant System (MMAS). The Computational
results show the superiority of IMACO. The results comparison shows that IMACO with the proposed
techniques suffers less from stagnation than the best known ant algorithms of ACS and MMAS.
Keywords: Ant colony optimization, combinatorial
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