152 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
Clustering using Vector Membership: An Extension of the Fuzzy C-Means Algorithm
Clustering is an important facet of explorative data mining and finds
extensive use in several fields. In this paper, we propose an extension of the
classical Fuzzy C-Means clustering algorithm. The proposed algorithm,
abbreviated as VFC, adopts a multi-dimensional membership vector for each data
point instead of the traditional, scalar membership value defined in the
original algorithm. The membership vector for each point is obtained by
considering each feature of that point separately and obtaining individual
membership values for the same. We also propose an algorithm to efficiently
allocate the initial cluster centers close to the actual centers, so as to
facilitate rapid convergence. Further, we propose a scheme to achieve crisp
clustering using the VFC algorithm. The proposed, novel clustering scheme has
been tested on two standard data sets in order to analyze its performance. We
also examine the efficacy of the proposed scheme by analyzing its performance
on image segmentation examples and comparing it with the classical Fuzzy
C-means clustering algorithm.Comment: 6 pages, 8 figures and 1 table (Conference Paper
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