Journal ArticleDOI
Runtime Analysis of Ant Colony Optimization with Best-So-Far Reinforcement
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TLDR
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.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.read more
Citations
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Book ChapterDOI
Ant Colony Optimization: Overview and Recent Advances
Marco Dorigo,Thomas Stützle +1 more
TL;DR: This chapter reviews developments in ACO and gives an overview of recent research trends, including the development of high-performing algorithmic variants and theoretical understanding of properties of ACO algorithms.
Ant Colony Optimization.
Marco Dorigo,Mauro Birattari +1 more
TL;DR: The goal of this article is to introduce ant colony optimization and to survey its most notable applications.
Journal ArticleDOI
A New Method for Lower Bounds on the Running Time of Evolutionary Algorithms
TL;DR: A new method based on fitness-level partitions and an additional condition on transition probabilities between fitness levels allows us to determine the optimal mutation-based algorithm for LO and OneMax, i.e., the algorithm that minimizes the expected number of fitness evaluations.
Posted Content
A New Method for Lower Bounds on the Running Time of Evolutionary Algorithms
TL;DR: In this article, the authors present a method for proving lower bounds on the expected running time of evolutionary algorithms based on fitness-level partitions and an additional condition on transition probabilities between fitness levels, which yields exact or nearexact lower bounds for LO, OneMax, long k-paths, and all functions with a unique optimum.
Journal ArticleDOI
Runtime Analysis of an Ant Colony Optimization Algorithm for TSP Instances
TL;DR: The expected runtime bounds for (1 + 1) MMAA on two TSP instances of complete and non-complete graphs are obtained and the influence of the parameters controlling the relative importance of pheromone trail versus visibility is analyzed.
References
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Journal ArticleDOI
Ant system: optimization by a colony of cooperating agents
TL;DR: It is shown how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling, and the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
Book
Ant Colony Optimization
TL;DR: Ant colony optimization (ACO) is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals as discussed by the authors In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization.
Proceedings ArticleDOI
On genetic algorithms
TL;DR: C Culling is near optimal for this problem, highly noise tolerant, and the best known a~~roach in some regimes, and some new large deviation bounds on this submartingale enable us to determine the running time of the algorithm.
Journal ArticleDOI
MAX-MIN Ant system
Thomas Stützle,Holger H. Hoos +1 more
TL;DR: Computational results on the Traveling Salesman Problem and the Quadratic Assignment Problem show that MM AS is currently among the best performing algorithms for these problems.
Book ChapterDOI
Ant Colony Optimization
TL;DR: Ant Colony Optimization (ACO) is a stochastic local search method that has been inspired by the pheromone trail laying and following behavior of some ant species as discussed by the authors.