We present two algorithms within the framework of the Ant Colony Optimization (ACO) metaheuristic. The rst algorithm seeks to increase the exploration bias of Gambardella et al.'s (2012) Enhanced Ant Colony System (EACS) model, a model which heavily increases the exploitation bias of the already highly exploitative ACS model in order to gain the bene t of increased speed. Our algorithm aims to strike a balance between these two models. The second is also an extension of EACS, based on Jayadeva et al.'s (2013) EigenAnt algorithm. EigenAnt aims to avoid the problem of stagnation found in ACO algorithms by, among other unique properties, utilizing a selective rather than global pheromone evaporation model, and by discarding heuristics in the solution construction phase. A performance comparison between our two models, the legacy ACS model, and the EACS model is presented. The Sequential Ordering Problem (SOP), one of the main problems used to demonstrate EACS, and one still actively studied to this day, was utilized to conduct the comparison.
Computer Science & Engineering Department
MS in Computer Science
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(2014).Ant Colony Optimization approaches for the
Sequential Ordering Problem [Master's Thesis, the American University in Cairo]. AUC Knowledge Fountain.
Ezzar, Ahmed Mohamed Alaa El-din. Ant Colony Optimization approaches for the
Sequential Ordering Problem. 2014. American University in Cairo, Master's Thesis. AUC Knowledge Fountain.