An Extended Ant Colony Algorithm and Its Convergence Analysis

Giovanni Sebastiani () and Giovanni Luca Torrisi ()
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Giovanni Sebastiani: Consiglio Nazionale delle Ricerche
Giovanni Luca Torrisi: Consiglio Nazionale delle Ricerche

Methodology and Computing in Applied Probability, 2005, vol. 7, issue 2, 249-263

Abstract: Abstract In this work, we propose a stochastic algorithm for solving $$ \mathcal{N}{\wp } - hard $$ combinatorial optimization problems. The procedure is formulated within the Ant Colony Optimization (ACO) framework, and extends the so-called Graph-based Ant System with time-dependent evaporation factor, (GBAS/tdev) studied in Gutjahr (2002). In particular, we consider an ACO search procedure which also takes into account the objective function value. We provide a rigorous theoretical study on the convergence of the proposed algorithm. Further, for a toy example, we compare by simulation the rate of convergence of the proposed algorithm with those from the Random Search (RS) and from the corresponding procedure in Gutjahr (2002).

Keywords: ant colony; convergence analysis; simulation; stochastic optimization (search for similar items in EconPapers)
Date: 2005
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DOI: 10.1007/s11009-005-1485-z

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