Runtime Analysis of Ant Colony Optimization with Best-So-Far Reinforcement

Walter J. Gutjahr and Giovanni Sebastiani ()
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Walter J. Gutjahr: University of Vienna
Giovanni Sebastiani: CNR

Methodology and Computing in Applied Probability, 2008, vol. 10, issue 3, 409-433

Abstract: Abstract The paper provides some theoretical results on the analysis of the expected time needed by a class of Ant Colony Optimization algorithms to solve combinatorial optimization problems. A part of the study refers to some general results on the expected runtime of the considered class of algorithms. These results are then specialized to the case of pseudo-Boolean functions. In particular, three well known functions and a combination of two of them are considered: the OneMax, the Needle-in-a-Haystack, the LeadingOnes, and the OneMax-Needle-in-a-Haystack. The results obtained for these functions are also compared to those from the well-investigated (1+1)-Evolutionary Algorithm. The results shed light on a suitable parameter choice for the considered class of algorithms. Furthermore, it turns out that for two of the four studied problems, the expected runtime for the considered class, expressed in terms of the problem size, is of the same order as that for (1+1)-Evolutionary Algorithm. For the other two problems, the results are significantly in favour of the considered class of Ant Colony Optimization algorithms.

Keywords: Analysis of algorithms; Combinatorial optimization; Stochastic algorithms; Hitting times; Markov processes; Primary 68W40; Secondary 60J20; 68W20 (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11009-007-9047-1

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