Application of Ant Colony Optimization and Harmony Search Algorithms to Reconfiguration of Radial Distribution Networks with Distributed Generations

Abdelaziz, Almoataz Y.; Osama, Reham A.; Elkhodary, Salem M.

doi:10.1166/jbic.2012.1007

Cited by 19 publications

(7 citation statements)

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“…A variety of metaheuristics have been designed to address problems that cannot be suitably tackled through more traditional optimisation algorithms -see (Yu et al, 2013;Zhong et al, 2012;Yang and Deb, 2012;Garcia-Gonzalo and Fernandez-Martinez, 2012;Natarajan et al, 2012;Ali and Sabat, 2012;Abdelaziz et al, 2012). In multi-modal optimisation problems, such as those tackled when optimising the C 3 E-SL parameters, it is of particular interest to use algorithms capable of escaping from local optima, hopefully being able to reach the global optimum -or at least local optimum solutions (within a reasonable computation time).…”

Section: A De Algorithm To Optimise the C E-sl Parametersmentioning

confidence: 99%

A differential evolution algorithm to optimise the combination of classifier and cluster ensembles

Coletta

Hruschka

Acharya

et al. 2015

IJBIC

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Unsupervised models can provide supplementary soft constraints to help classify new data since similar instances are more likely to share the same class label. In this context, this paper reports on a study on how to make an existing algorithm, named C 3 E (from consensus between classification and clustering ensembles), more convenient by automatically tuning its main parameters. The C 3 E algorithm is based on a general optimisation framework that takes as input class membership estimates from existing classifiers, and a similarity matrix from a cluster ensemble operating solely on the new (target) data to be classified, in order to yield a consensus labelling of the new data. To do so, two parameters have to be defined a priori by the user: the relative importance of classifier and cluster ensembles, and the number of iterations of the algorithm. We propose a differential evolution (DE) algorithm, named dynamic DE (D 2 E), which is a computationally efficient alternative for optimising such parameters. D 2 E provides better results than DE by dynamically updating its control parameters. Moreover, competitive results were achieved when comparing D 2 E with three state-of-the-art algorithms.

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Section: A De Algorithm To Optimise the C E-sl Parametersmentioning

confidence: 99%

A differential evolution algorithm to optimise the combination of classifier and cluster ensembles

Coletta

Hruschka

Acharya

et al. 2015

IJBIC

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“…Moreover, ACO is applied in some other areas for problem solving and optimisation. Classification rules (Martens et al, 2006), Bayesian networks (DeCampos et al, 2002(DeCampos et al, , 2008, protein folding (Shmygelska and Hoos, 2005), radial distribution networks (Abdelaziz et al, 2012), hardware optimisation (Duan et al, 2012) and data mining (Parpinelli et al, 2002) are some examples of these areas. More information about applications of ACO can be found in study of ChandraMohan and Baskaran (2012) and Dorigo and Stutzle (2004).…”

Section: Applications Of Acomentioning

confidence: 99%

Ant colony optimisation for vehicle traffic systems: applications and challenges

Jabbarpour

Malakooti

Noor

et al. 2014

IJBIC

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Ant-based algorithms simulate the cooperative behaviour of real ants in finding food resources. A significant number of studies have focused on the self-organised behaviour of ants in the natural environment to develop effective systems for dynamic problems. Ant-based systems have special properties such as scalability, adaptability, and dynamicity, which are the main requirements for solving vehicle traffic congestion problem. Thus, ant-based algorithms are now being adopted by vehicle traffic systems (VTSs) to guide vehicles to less congested paths. However, literature shows that comprehensive reviews are lacking in this field. The main contribution of this paper is the review and classification of the most relevant systems based on novel taxonomy. A survey that includes statistical analyses on ant-based VTS was conducted to identify the limitations and evaluation process of VTS. This paper concludes by proposing a general framework in applying ant colony optimisation to VTS. . He received his Master of Data Communication and Networking in 2012 at the same university. His current research interests include ad hoc networks, quality of service, swarm intelligence and vehicle traffic routing systems.Hossein Malakooti received his Master of Data Communication and Networking at the

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“…Inspired by foraging behavior of ants, Ant Colony Optimization (ACO) [1] was firstly proposed by M. Dorigo and his colleagues in the 1990s. It has been applied to many combinatorial optimization problems [2][3][4]. In order to broaden the application field of ACO, BACO was proposed [5], which has been applied to many application fields, from continual problems to discrete problems, such as function optimization problem, 0/1 knapsack problem and so on [6,7].…”

Section: Introductionmentioning

confidence: 99%

Binary Ant Colony Algorithm with Particle Optimization Feature for Attribute Reduction Problem

Ni¹

2015

J. Inf. Comput. Sci.

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According to the lack of initial pheromone which leads to the long searching time of the Binary Ant Colony algorithm (BACO), a novel method named Binary Ant Colony Algorithm with Particle Optimization Feature (PBACO) is proposed in this paper. Starting from one-dimensional cellular automata model, the initial solution is produced by Binary Particle Swarm Optimization (BPSO). Then mapping mechanism is introduced to convert the position of particle into pheromone, in this way, a path with distinct discrepancy level of pheromone is generated in the shortest time, and the total searching time of BACO is greatly reduced. At last, PBACO combined with fractal dimension is utilized to solve attribute reduction problem. Six datasets are used to conduct experiments. Experimental results show that our proposed method has comparatively high classification accuracy.

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Application of Ant Colony Optimization and Harmony Search Algorithms to Reconfiguration of Radial Distribution Networks with Distributed Generations

Cited by 19 publications

References 0 publications

A differential evolution algorithm to optimise the combination of classifier and cluster ensembles

A differential evolution algorithm to optimise the combination of classifier and cluster ensembles

Ant colony optimisation for vehicle traffic systems: applications and challenges

Binary Ant Colony Algorithm with Particle Optimization Feature for Attribute Reduction Problem

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