Future Generation Computer Systems
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Theoretical Aspects of Evolutionary Algorithms
ICALP '01 Proceedings of the 28th International Colloquium on Automata, Languages and Programming,
Ant Colony Optimization
A study of drift analysis for estimating computation time of evolutionary algorithms
Natural Computing: an international journal
A rigorous analysis of the compact genetic algorithm for linear functions
Natural Computing: an international journal
On the runtime analysis of the 1-ANT ACO algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A Blend of Markov-Chain and Drift Analysis
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
How Single Ant ACO Systems Optimize Pseudo-Boolean Functions
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
When is an estimation of distribution algorithm better than an evolutionary algorithm?
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Ant Colony Optimization and the minimum spanning tree problem
Theoretical Computer Science
Analysis of computational time of simple estimation of distribution algorithms
IEEE Transactions on Evolutionary Computation
A few ants are enough: ACO with iteration-best update
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Theoretical properties of two ACO approaches for the traveling salesman problem
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
IEEE Transactions on Evolutionary Computation
Fitness-levels for non-elitist populations
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Running time analysis of Ant Colony Optimization for shortest path problems
Journal of Discrete Algorithms
Ants easily solve stochastic shortest path problems
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Computational complexity analysis of multi-objective genetic programming
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
ACO beats EA on a dynamic pseudo-boolean function
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Black-box complexities of combinatorial problems
Theoretical Computer Science
Optimizing expected path lengths with ant colony optimization using fitness proportional update
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
| Hi-index | 0.00 |
With this paper, we contribute to the understanding of ant colony optimization (ACO) algorithms by formally analyzing their runtime behavior. We study simple MAX-MIN ant systems on the class of linear pseudo-Boolean functions defined on binary strings of length n. Our investigations point out how the progress according to function values is stored in the pheromones. We provide a general upper bound of O((n3 log n)ρ) on the running time for two ACO variants on all linear functions, where ρ determines the pheromone update strength. Furthermore, we show improved bounds for two well-known linear pseudo-Boolean functions called ONEMAX and BINVAL and give additional insights using an experimental study.