6,079 research outputs found
A Swarm intelligence approach for biometrics verification and identification
In this paper we investigate a swarm intelligence classification
approach for both biometrics verification and identification
problems. We model the problem by representing biometric templates as
ants, grouped in colonies representing the clients of a biometrics
authentication system. The biometric template classification process
is modeled as the aggregation of ants to colonies. When test input
data is captured -- a new ant in our representation -- it will be
influenced by the deposited phermonones related to the population of
the colonies.
We experiment with the Aggregation Pheromone density based Classifier
(APC), and our results show that APC outperforms ``traditional''
techniques -- like 1-nearest-neighbour and Support Vector Machines --
and we also show that performance of APC are comparable to several
state of the art face verification algorithms. The results here
presented let us conclude that swarm intelligence approaches represent
a very promising direction for further investigations for biometrics
verification and identification
Application of ant colony optimisation in distribution transformer sizing
This study proposes an optimisation method for transformer sizing in power system using ant colony optimisation and a verification of the process by MATLAB software. The aim is to address the issue of transformer sizing which is a major challenge affecting its effective performance, longevity, huge capital cost and power loss. This method accounts for the constraints imposed by the load capacity and the thermal overload that the transformer serves throughout its lifetime. The objective function to be minimised includes the transformer capital cost as well as the energy loss cost. In this paper, the Optimal Transformer Sizing (OTS) problem which is fundamentally the basic routine for the location of transformer was addressed by means of the heuristic Ant System Method using the Elitist strategy, called Elitist Any System (EAS). EAS belong to the family of Ant Colony Optimisation (ACO) algorithm. ACO when appropriately applied determines the least cost path, taking into consideration the various essential factors including transformer bid price, growth rate, inflation rate, peak load, thermal deviation and energy loss cost. The study demonstrated a significant saving in capital cost using this approach as evidenced from the changes to the transformer following the initial installed capacity of 190kVA to 320kVA in the second stage and then finally to 630kVA in the third stage which effectively supported the remaining period under consideration. This finding is in contrast to the traditional simplified sizing strategy usually adopted by utilities companies.Keywords: ant colony, optimization, transformer sizing, distribution transforme
Is swarm intelligence able to create mazes?
In this paper, the idea of applying Computational Intelligence in the process
of creation board games, in particular mazes, is presented. For two different
algorithms the proposed idea has been examined. The results of the experiments
are shown and discussed to present advantages and disadvantages
Secure and robust multi-constrained QoS aware routing algorithm for VANETs
Secure QoS routing algorithms are a fundamental part of wireless networks that aim to provide services with QoS and security guarantees. In Vehicular Ad hoc Networks (VANETs), vehicles perform routing functions, and at the same time act as end-systems thus routing control messages are transmitted unprotected over wireless channels. The QoS of the entire network could be degraded by an attack on the routing process, and manipulation of the routing control messages. In this paper, we propose a novel secure and reliable multi-constrained QoS aware routing algorithm for VANETs. We employ the Ant Colony Optimisation (ACO) technique to compute feasible routes in VANETs subject to multiple QoS constraints determined by the data traffic type. Moreover, we extend the VANET-oriented Evolving Graph (VoEG) model to perform plausibility checks on the exchanged routing control messages among vehicles. Simulation results show that the QoS can be guaranteed while applying security mechanisms to ensure a reliable and robust routing service
Optimal power harness routing for small-scale satellites
This paper presents an approach to optimal power harness design based on a modified ant colony optimisation algorithm. The optimisation of the harness routing topology is formulated as a constrained multi-objective optimisation problem in which the main objectives are to minimise the length (and therefore the mass) of the harness. The modified ant colony optimisation algorithm automatically routes different types of wiring, creating the optimal harness layout. During the optimisation the length, mass and bundleness of the cables are computed and used as cost functions. The optimisation algorithm works incrementally on a finite set of waypoints, forming a tree, by adding and evaluating one branch at a time, utilising a set of heuristics using the cable length and cable bundling as criteria to select the optimal path. Constraints are introduced as forbidden waypoints through which digital agents (hereafter called ants) cannot travel. The new algorithm developed will be applied to the design of the harness of a small satellite, with results highlighting the capabilities and potentialities of the code
An improved Ant Colony System for the Sequential Ordering Problem
It is not rare that the performance of one metaheuristic algorithm can be
improved by incorporating ideas taken from another. In this article we present
how Simulated Annealing (SA) can be used to improve the efficiency of the Ant
Colony System (ACS) and Enhanced ACS when solving the Sequential Ordering
Problem (SOP). Moreover, we show how the very same ideas can be applied to
improve the convergence of a dedicated local search, i.e. the SOP-3-exchange
algorithm. A statistical analysis of the proposed algorithms both in terms of
finding suitable parameter values and the quality of the generated solutions is
presented based on a series of computational experiments conducted on SOP
instances from the well-known TSPLIB and SOPLIB2006 repositories. The proposed
ACS-SA and EACS-SA algorithms often generate solutions of better quality than
the ACS and EACS, respectively. Moreover, the EACS-SA algorithm combined with
the proposed SOP-3-exchange-SA local search was able to find 10 new best
solutions for the SOP instances from the SOPLIB2006 repository, thus improving
the state-of-the-art results as known from the literature. Overall, the best
known or improved solutions were found in 41 out of 48 cases.Comment: 30 pages, 8 tables, 11 figure
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