How Single Ant ACO Systems Optimize Pseudo-Boolean Functions

Doerr, Benjamin; Johannsen, Daniel; Tang, Ching Hoo

doi:10.1007/978-3-540-87700-4_38

Cited by 9 publications

(4 citation statements)

References 12 publications

(26 reference statements)

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“…which com from literature [10] that use basic max-min ant system for solving Boolean function. In the experiment we set the largest number of iterations max 1000000 t =…”

Section: Methodsmentioning

confidence: 99%

Modified Max-Min Ant Colony System for Solving Pseudo-Boolean Optimization Problem

Liu

Ren

2009

2009 Second International Conference on Future Information Technology and Management Engineering

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Based on the max-min ant system solving the Pseudo-Boolean Optimization problem, we improved the process of solving the problem .An memory array is introduced to record changes of each bit string, memory array to be used for optimization of the solution of the current iteration. Such improvements will help improve the convergence rate of Max-Min ant colony algorithm .The improved algorithm was applied to solve the problem of Pseudo-Boolean functions , Experiments show a reduction in the number of iteration before achieving the optimal which indicating the effectiveness and Superiority of the algorithm.

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“…which com from literature [10] that use basic max-min ant system for solving Boolean function. In the experiment we set the largest number of iterations max 1000000 t =…”

Section: Methodsmentioning

confidence: 99%

Modified Max-Min Ant Colony System for Solving Pseudo-Boolean Optimization Problem

Liu

Ren

2009

2009 Second International Conference on Future Information Technology and Management Engineering

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“…Our experiments are related to those in [3,14]. The authors of the first article concentrate their analyses of 1-ANT and MMAS on OneMax, LeadingOnes, and random linear functions.…”

Section: Methodsmentioning

confidence: 99%

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

Kötzing

Neumann

Sudholt

et al. 2011

Proceedings of the 11th Workshop Proceedings on Foundations of Genetic Algorithms

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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((n 3 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.

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“…Related to our experiments are those in [1,11]. The authors of the first article concentrate their analyses of 1-ANT and MMAS on OneMax, Leading-Ones, and random linear functions.…”

Section: Methodsmentioning

confidence: 99%

Simple Max-Min Ant Systems and the Optimization of Linear Pseudo-Boolean Functions

Kötzing¹,

Neumann²,

Sudholt³

et al. 2010

Preprint

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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 pheromone. We provide a general upper bound of O((n 3 log n)/ρ) for two ACO variants on all linear functions, where ρ determines the pheromone update strength. Furthermore, we show improved bounds for two wellknown linear pseudo-Boolean functions called OneMax and BinVal and give additional insights using an experimental study.

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How Single Ant ACO Systems Optimize Pseudo-Boolean Functions

Cited by 9 publications

References 12 publications

Modified Max-Min Ant Colony System for Solving Pseudo-Boolean Optimization Problem

Modified Max-Min Ant Colony System for Solving Pseudo-Boolean Optimization Problem

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

Simple Max-Min Ant Systems and the Optimization of Linear Pseudo-Boolean Functions

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