How Single Ant ACO Systems Optimize Pseudo-Boolean Functions

Authors:
Benjamin Doerr;Daniel Johannsen;Ching Hoo Tang
Affiliations:
Max-Planck-Institut für Informatik, Saarbrücken, Germany;Max-Planck-Institut für Informatik, Saarbrücken, Germany;Max-Planck-Institut für Informatik, Saarbrücken, Germany
Venue:
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Year:
2008

Citing 8
Cited 4

MAX-MIN Ant system

Future Generation Computer Systems
On the analysis of the (1+ 1) evolutionary algorithm

Theoretical Computer Science
Ant Colony Optimization

Ant Colony Optimization
On the runtime analysis of the 1-ANT ACO algorithm

Proceedings of the 9th annual conference on Genetic and evolutionary computation
First steps to the runtime complexity analysis of ant colony optimization

Computers and Operations Research
Rigorous Analyses for the Combination of Ant Colony Optimization and Local Search

ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Comparing variants of MMAS ACO algorithms on pseudo-boolean functions

SLS'07 Proceedings of the 2007 international conference on Engineering stochastic local search algorithms: designing, implementing and analyzing effective heuristics
Runtime analysis of a simple ant colony optimization algorithm

ISAAC'06 Proceedings of the 17th international conference on Algorithms and Computation

Runtime analysis of the 1-ANT ant colony optimizer

Theoretical Computer Science
Simple max-min ant systems and the optimization of linear pseudo-boolean functions

Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Theory of swarm intelligence

Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Theory of swarm intelligence

Proceedings of the 14th annual conference companion on Genetic and evolutionary computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

We undertake a rigorous experimental analysis of the optimization behavior of the two most studied single ant ACO systems on several pseudo-boolean functions. By tracking the behavior of the underlying random processes rather than just regarding the resulting optimization time, we gain additional insight into these systems. A main finding is that in those cases where the single ant ACO system performs well, it basically simulates the much simpler (1+1) evolutionary algorithm.