9,472 research outputs found
A Deterministic Model for Analyzing the Dynamics of Ant System Algorithm and Performance Amelioration through a New Pheromone Deposition Approach
Ant Colony Optimization (ACO) is a metaheuristic for solving difficult
discrete optimization problems. This paper presents a deterministic model based
on differential equation to analyze the dynamics of basic Ant System algorithm.
Traditionally, the deposition of pheromone on different parts of the tour of a
particular ant is always kept unvarying. Thus the pheromone concentration
remains uniform throughout the entire path of an ant. This article introduces
an exponentially increasing pheromone deposition approach by artificial ants to
improve the performance of basic Ant System algorithm. The idea here is to
introduce an additional attracting force to guide the ants towards destination
more easily by constructing an artificial potential field identified by
increasing pheromone concentration towards the goal. Apart from carrying out
analysis of Ant System dynamics with both traditional and the newly proposed
deposition rules, the paper presents an exhaustive set of experiments performed
to find out suitable parameter ranges for best performance of Ant System with
the proposed deposition approach. Simulations reveal that the proposed
deposition rule outperforms the traditional one by a large extent both in terms
of solution quality and algorithm convergence. Thus, the contributions of the
article can be presented as follows: i) it introduces differential equation and
explores a novel method of analyzing the dynamics of ant system algorithms, ii)
it initiates an exponentially increasing pheromone deposition approach by
artificial ants to improve the performance of algorithm in terms of solution
quality and convergence time, iii) exhaustive experimentation performed
facilitates the discovery of an algebraic relationship between the parameter
set of the algorithm and feature of the problem environment.Comment: 4th IEEE International Conference on Information and Automation for
Sustainability, 200
Convergence results for continuous-time dynamics arising in ant colony optimization
This paper studies the asymptotic behavior of several continuous-time
dynamical systems which are analogs of ant colony optimization algorithms that
solve shortest path problems. Local asymptotic stability of the equilibrium
corresponding to the shortest path is shown under mild assumptions. A complete
study is given for a recently proposed model called EigenAnt: global asymptotic
stability is shown, and the speed of convergence is calculated explicitly and
shown to be proportional to the difference between the reciprocals of the
second shortest and the shortest paths.Comment: A short version of this paper was published in the preprints of the
19th World Congress of the International Federation of Automatic Control,
Cape Town, South Africa, 24-29 August 201
Balancing building and maintenance costs in growing transport networks
The costs associated to the length of links impose unavoidable constraints to
the growth of natural and artificial transport networks. When future network
developments can not be predicted, building and maintenance costs require
competing minimization mechanisms, and can not be optimized simultaneously.
Hereby, we study the interplay of building and maintenance costs and its impact
on the growth of transportation networks through a non-equilibrium model of
network growth. We show cost balance is a sufficient ingredient for the
emergence of tradeoffs between the network's total length and transport
effciency, of optimal strategies of construction, and of power-law temporal
correlations in the growth history of the network. Analysis of empirical ant
transport networks in the framework of this model suggests different ant
species may adopt similar optimization strategies.Comment: 4 pages main text, 2 pages references, 4 figure
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