13 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
A Study of the Grunwald-Letnikov Definition for Minimizing the Effects of Random Noise on Fractional Order Differential Equations
Of the many definitions for fractional order differintegral, the
Grunwald-Letnikov definition is arguably the most important one. The necessity
of this definition for the description and analysis of fractional order systems
cannot be overstated. Unfortunately, the Fractional Order Differential Equation
(FODE) describing such a systems, in its original form, highly sensitive to the
effects of random noise components inevitable in a natural environment. Thus
direct application of the definition in a real-life problem can yield erroneous
results. In this article, we perform an in-depth mathematical analysis the
Grunwald-Letnikov definition in depth and, as far as we know, we are the first
to do so. Based on our analysis, we present a transformation scheme which will
allow us to accurately analyze generalized fractional order systems in presence
of significant quantities of random errors. Finally, by a simple experiment, we
demonstrate the high degree of robustness to noise offered by the said
transformation and thus validate our scheme.Comment: 4th IEEE International Conference on Information and Automation for
Sustainability, 200
Complete Identification of a Dynamic Fractional Order System Under Non-ideal Conditions Using Fractional Differintegral Definitions
This contribution deals with identification of fractional-order dynamical
systems. System identification, which refers to estimation of process
parameters, is a necessity in control theory. Real processes are usually of
fractional order as opposed to the ideal integral order models. A simple and
elegant scheme of estimating the parameters for such a fractional order process
is proposed. This method employs fractional calculus theory to find equations
relating the parameters that are to be estimated, and then estimates the
process parameters after solving the simultaneous equations. The data used for
the calculations are intentionally corrupted to simulate real-life conditions.
Results show that the proposed scheme offers a very high degree of accuracy
even for erroneous data.Comment: 16th IEEE International Conference on Advanced Computing and
Communication, 200
A Novel Parser Design Algorithm Based on Artificial Ants
This article presents a unique design for a parser using the Ant Colony
Optimization algorithm. The paper implements the intuitive thought process of
human mind through the activities of artificial ants. The scheme presented here
uses a bottom-up approach and the parsing program can directly use ambiguous or
redundant grammars. We allocate a node corresponding to each production rule
present in the given grammar. Each node is connected to all other nodes
(representing other production rules), thereby establishing a completely
connected graph susceptible to the movement of artificial ants. Each ant tries
to modify this sentential form by the production rule present in the node and
upgrades its position until the sentential form reduces to the start symbol S.
Successful ants deposit pheromone on the links that they have traversed
through. Eventually, the optimum path is discovered by the links carrying
maximum amount of pheromone concentration. The design is simple, versatile,
robust and effective and obviates the calculation of the above mentioned sets
and precedence relation tables. Further advantages of our scheme lie in i)
ascertaining whether a given string belongs to the language represented by the
grammar, and ii) finding out the shortest possible path from the given string
to the start symbol S in case multiple routes exist.Comment: 4th IEEE International Conference on Information and Automation for
Sustainability, 200