27,662 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
An Improved Excitation Matching Method based on an Ant Colony Optimization for Suboptimal-Free Clustering in Sum-Difference Compromise Synthesis
Dealing with an excitation matching method, this paper presents a global optimization strategy for the optimal clustering in sum-difference compromise linear arrays. Starting from a combinatorial formulation of the problem at hand, the proposed technique is aimed at determining the sub-array configuration expressed as the optimal path inside a directed acyclic graph structure modelling the solution space. Towards this end, an ant colony metaheuristic is used to benefit of its hill-climbing properties in dealing with the non-convexity of the sub-arraying as well as in managing graph searches. A selected set of numerical experiments are reported to assess the efficiency and current limitations of the ant-based strategy also in comparison with previous local combinatorial search methods. (c) 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works
Parallel ACO with a Ring Neighborhood for Dynamic TSP
The current paper introduces a new parallel computing technique based on ant
colony optimization for a dynamic routing problem. In the dynamic traveling
salesman problem the distances between cities as travel times are no longer
fixed. The new technique uses a parallel model for a problem variant that
allows a slight movement of nodes within their Neighborhoods. The algorithm is
tested with success on several large data sets.Comment: 8 pages, 1 figure; accepted J. Information Technology Researc
An ant colony optimization approach for maximizing the lifetime of heterogeneous wireless sensor networks
Maximizing the lifetime of wireless sensor networks (WSNs) is a challenging problem. Although some methods exist to address the problem in homogeneous WSNs, research on this problem in heterogeneous WSNs have progressed at a slow pace. Inspired by the promising performance of ant colony optimization (ACO) to solve combinatorial problems, this paper proposes an ACO-based approach that can maximize the lifetime of heterogeneous WSNs. The methodology is based on finding the maximum number of disjoint connected covers that satisfy both sensing coverage and network connectivity. A construction graph is designed with each vertex denoting the assignment of a device in a subset. Based on pheromone and heuristic information, the ants seek an optimal path on the construction graph to maximize the number of connected covers. The pheromone serves as a metaphor for the search experiences in building connected covers. The heuristic information is used to reflect the desirability of device assignments. A local search procedure is designed to further improve the search efficiency. The proposed approach has been applied to a variety of heterogeneous WSNs. The results show that the approach is effective and efficient in finding high-quality solutions for maximizing the lifetime of heterogeneous WSNs
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