5 research outputs found
An improved Ant Colony System for the Sequential Ordering Problem
It is not rare that the performance of one metaheuristic algorithm can be
improved by incorporating ideas taken from another. In this article we present
how Simulated Annealing (SA) can be used to improve the efficiency of the Ant
Colony System (ACS) and Enhanced ACS when solving the Sequential Ordering
Problem (SOP). Moreover, we show how the very same ideas can be applied to
improve the convergence of a dedicated local search, i.e. the SOP-3-exchange
algorithm. A statistical analysis of the proposed algorithms both in terms of
finding suitable parameter values and the quality of the generated solutions is
presented based on a series of computational experiments conducted on SOP
instances from the well-known TSPLIB and SOPLIB2006 repositories. The proposed
ACS-SA and EACS-SA algorithms often generate solutions of better quality than
the ACS and EACS, respectively. Moreover, the EACS-SA algorithm combined with
the proposed SOP-3-exchange-SA local search was able to find 10 new best
solutions for the SOP instances from the SOPLIB2006 repository, thus improving
the state-of-the-art results as known from the literature. Overall, the best
known or improved solutions were found in 41 out of 48 cases.Comment: 30 pages, 8 tables, 11 figure
Ant Colony Optimization approaches for the Sequential Ordering Problem
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.\u27s (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.\u27s (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