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8th International Conference on Computer Science and Information Technology (CSIT)

作者： Yasen, Mais Al-Madi, Nailah Obeid, Nadim Princess Sumaya Univ Technol Dept Comp Sci Amman Jordan Univ Jordan King Abdullah II Sch IT Amman Jordan

ISBN: (纸本)9781538641521

As the early diagnoses helps in curing most diseases or in making them more bearable, it increases the need to build a good prediction model. Artificial Neural Network (ANN) is one of the evolutionary computation techniques that can be used as a prediction model for new data records. The ANN training method implemented is backpropagation which can be used in conjunction with an optimization method such as dragonfly algorithm (DA) and Artificial Bee Colony (ABC) algorithm. ABC has been used in the optimization of synaptic weights from an ANN. This research presents the use of DA as an optimization algorithm for the weights of each connection between the ANN neurons in a model called (ANN-DA). A disease prediction model that makes use of ANN-DA and takes real data as a case study is presented. We shall also present a model that makes use of ABC, ANN-ABC, in order to evaluate ANN-DA. An experimental comparison of both optimization algorithms with other well-known classifiers has been made using different metrics. The results show that ANN-DA proved its efficiency over ANN-ABC and regular ANN. Furthermore, ANN-DA results were more robust for all datasets.

关键词： Machine Learning Evolutionary Computation Artificial Bee Colony algorithm dragonfly algorithm Artificial Neural Network Optimization Disease Prediction

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Optimal Allocation of Renewable Distributed Generators and Capacitors in Distribution System using dragonfly algorithm 2

Optimal Allocation of Renewable Distributed Generators and C...

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2nd International Conference on Intelligent Circuits and Systems (ICICS)

作者： Sudabattula, Suresh Kumar Kowsalya, M. Suresh, Velamuri Melimi, Ravi Kumar Lovely Profess Univ Sch Elect & Elect Engn Phagwara India VIT Univ Sch Elect Engn Vellore Tamil Nadu India PVP Siddartha Inst Technol Dept Elect & Elect Engn Vijayawada India

ISBN: (纸本)9781538664834

Better integration of capacitors and renewable distributed generators (DGs) in distribution system is a prominent problem in present era. In this paper a systematic approach is presented to solve the DER allocation problem with an aim of improving voltage profile as well as power loss reduction. An integrated approach of loss sensitivity factor and dragonfly algorithm is presented to solve the problem. Developed approach is tested on large scale 119 bus system. Different cases are considered for analysis. Further, the obtained result of developed method is compared with simulated annealing technique for validation.

关键词： Renewable Distributed Generators Capacitors Loss Senstivity Factor Power loss dragonfly algorithm

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UNIQUE dragonfly OPTIMIZATION algorithm FOR HARVESTING AND CLUSTERING THE KEY FEATURES 18

UNIQUE DRAGONFLY OPTIMIZATION ALGORITHM FOR HARVESTING AND C...

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18th IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC)

作者： Devarakonda, Nagaraju Anandarao, Sarvani Kamarajugadda, Raviteja Wang, Yingxu LBR Coll Engn Mylavaram Andhra Pradesh India Univ Calgary Calgary AB Canada

ISBN: (纸本)9781728114194

In many applications, the feature selection plays an important role, as best feature can bring out the accurate results. The features selected must represent the entire dataset. Here we have considered "Sequential Forward Selection" for feature extraction and used refined dragonfly algorithm to approach and to migrate from the best and worst features respectively. We improvised the conventional dragonfly algorithm by adding the convergence and fitness functions. To access the accuracy of the algorithm we introduced the fitness function. This paper has discussed about the general hunting behaviour of the dragonfly and dragonfly algorithm (DA) with convergence and fitness functions. A comparative study was shown for the best search agent position between modified DA and traditional DA, at the same time test function values of refined dragonfly algorithm (RDA) is compared with whale optimization algorithm (WOA) and Tornadogenesis Optimization algorithm (TOA).We have evaluated refined DA on the 23 benchmark function corresponding values are shown in experiment.

关键词： dragonfly algorithm sequential forward selection convergence Ilinction benchmark functions Tornadogenesis Optimization algorithm whale optimization algorithm

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Assimilation of Optimal Sized Hybrid Photovoltaic-Biomass System by dragonfly algorithm with Grid

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ENERGIES 2018年第7期11卷 1892页

作者： Ghosh, Smarajit Karar, Vinod Thapar Inst Engn & Technol Dept Elect & Instrumentat Engn Patiala 147004 Punjab India CSIR Cent Sci Instruments Org Opt Devices & Syst Sect 30-C Chandigarh 160030 India

A growing interest in renewable energy resources has been observed for several years, due to their pollution-free nature, availability all over the world, and continuity. These facts make these energy resources attractive for many applications. In this work, the hybrid combination of a photovoltaic-biomass system is investigated as an energy source. This paper determines optimal sizing and cost reduction of grid-integrated renewable energy resources by using an intelligence optimization technique, the dragonfly algorithm. The efficiency of the proposed methodology is also compared with an existing technique, which uses the artificial bee colony (ABC) algorithm. The scope of this work is to reduce the annual total cost of power with a reduced number of solar panels. The monthly average solar radiation is used to compute the obtained power. The outcome of the proposed technique proves that the grid-connected system with an optimal number of components satisfactorily meets the needs of the village at a reduced price. The simulation results are carried out under the MATLAB environment. The comparison of results clearly demonstrates that the proposed system is much more efficient than the existing one.

关键词： Hybrid photovoltaic-biomass system power grid sizing of components cost reduction ABC algorithm dragonfly algorithm

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Memory based Hybrid dragonfly algorithm for numerical optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 83卷 63-78页

作者： Ranjini, Sree K. S. Murugan, S. Indira Gandhi Ctr Atom Res Homi Bhabha Natl Inst Kalpakkam Tamil Nadu India Indira Gandhi Ctr Atom Res Remote Handling Irradiat & Robot Div Kalpakkam Tamil Nadu India

dragonfly algorithm (DA) is a recently proposed optimization algorithm based on the static and dynamic swarming behaviour of dragonflies. Due to its simplicity and efficiency, DA has received interest of researchers from different fields. However, it lacks internal memory which may lead to its premature convergence to local optima. To overcome this drawback, we propose a novel Memory based Hybrid dragonfly algorithm (MHDA) for solving numerical optimization problems. The pbest and gbest concept of Particle Swarm optimization (PSO) is added to conventional DA to guide the search process for potential candidate solutions and PSO is then initialized with pbest of DA to further exploit the search space. The proposed method combines the exploration capability of DA and exploitation capability of PSO to achieve global optimal solutions. The efficiency of the MHDA is validated by testing on basic unconstrained benchmark functions and CEC 2014 test functions. A comparative performance analysis between MHDA and other powerful optimization algorithms have been carried out and significance of the results is proved by statistical methods. The results show that MHDA gives better performance than conventional DA and PSO. Moreover, it gives competitive results in terms of convergence, accuracy and search-ability when compared with the state-of-the-art algorithms. The efficacy of MHDA in solving real world problems is also explained with three engineering design problems. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： dragonfly algorithm Particle Swarm Optimization Hybridization Benchmark functions Engineering problems Friedman's test

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Using dragonfly algorithm for optimization of orthotropic infinite plates with a quasi-triangular cut-out

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EUROPEAN JOURNAL OF MECHANICS A-SOLIDS 2017年 66卷 1-14页

作者： Jafari, Mohammad Chaleshtari, Mohammad Hossein Bayati Shahrood Univ Technol Mech Engn Dept Shahrood Iran

This paper aims at optimizing the parameters involved in the stress analysis of perforated orthotropic plates, to achieve the lowest value of stress around the quasi-triangular cut-out located in an infinite orthotropic plate by using the dragonfly algorithm (DA) method. This method is used to calculate the stress distribution based on Lekhnitskii's analytical solution. The study design variables include fiber angle, load angle, bluntness, orientation angle of cut-out and the material properties. In addition, the performance of the DA algorithm is compared with the Genetic algorithm (GA) and Particle Swarm Optimization (PSO). The comparison of these methods indicates the appropriateness of the DA algorithm in optimizing the perforated plates. The finite element method has been used to verify the accuracy of the analytical results. The results indicate that by selecting the aforementioned parameters properly, we can increase the structural load-bearing capacity. (C) 2017 Elsevier Masson SAS. All rights reserved.

关键词： Quasi-triangular cut-out dragonfly algorithm Orthotropic plate Complex variable method

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Optimization of Size and Cost of Static VAR Compensator using dragonfly algorithm for Voltage Profile Improvement in Power Transmission Systems

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2018年第1期8卷 56-66页

作者： Vanishree, J. Ramesh, V. VIT Univ Sch Elect Engn Vellore 14 Tamil Nadu India

Voltage stability is a major concern in power transmission systems due to mismatch between power generation and demand Hence maintenance of voltage profile within the acceptable limit becomes a challenging task. In this paper the weak buses to implement the reactive power compensators are identified by applying eigenvalue decomposition technique on partitioned Y-admittance matrix. The dragonfly algorithm is used to optimize the size and cost of the SVC with the overload factor, voltage deviation and VAR regulation as the design constraints. The dragonfly algorithm is implemented on IEEE 14 and 30 bus systems and the results obtained with and without the placement of Static VAR Compensators are compared with other algorithms to show its effectiveness. To emphasize the possibility to integrate the renewable energy and to demonstrate its effectiveness a 2 MW solar generation is installed at one of the weakest buses of IEEE 30 bus system and the improvement in voltage profile is shown. The further scope of this work is to implement the wind generators at the weakest buses so as to reduce the electrical distance between the generators and the farthest load buses and to secure the system from voltage collapse.

关键词： Voltage profile Static VAR Compensator dragonfly algorithm Eigenvalue decomposition Y-admittance matrix wind generators

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A New Meta Heuristic dragonfly Optimizaion algorithm for Optimal Reactive Power Dispatch Problem

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GAZI UNIVERSITY JOURNAL OF SCIENCE 2018年第4期31卷 1107-1121页

作者： Palappan, Anbarasan Thangavelu, Jayabarathi VIT Univ Sch Elect Engn Vellore Tamil Nadu India

This research accesses a novel approach of utilising an advanced Meta-heuristic Optimization technique with a single objective to pledge with optimal reactive power dispatch problem in electrical power system network. The prime focus of reactive power dispatch is to curtail the total active power loss in transmission lines. In this detailed study, the dragonfly algorithm was realized on standard IEEE-14 bus and 30 bus systems. The outcome of dragonfly algorithm lucidly indicate the capablity of increasing the antecedent random population size for a liable global optimization problem, focalized close to the global optimum and contributing precise outcome results related to another popular algorithm.

关键词： Optimal reactive power dispatch dragonfly algorithm Real power loss minimization and Swarm intelligence

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Binary dragonfly algorithm for Feature Selection

Binary Dragonfly Algorithm for Feature Selection

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International Conference on New Trends in Computing Sciences (ICTCS)

作者： Mafarja, Majdi M. Eleyan, Derar Jaber, Iyad Mirjalili, Seyedali Hammouri, Abdelaziz Birzeit Univ Dept Comp Sci Birzeit West Bank Palestine Griffith Univ Sch Informat & Commun Technol Nathan Campus Brisbane Qld 4111 Australia Al Balqa Appl Univ Dept Comp Informat Syst Amman Jordan

ISBN: (纸本)9781538605271

Wrapper feature selection methods aim to reduce the number of features from the original feature set to and improve the classification accuracy simultaneously. In this paper, a wrapper-feature selection algorithm based on the binary dragonfly algorithm is proposed. dragonfly algorithm is a recent swarm intelligence algorithm that mimics the behavior of the dragonflies. Eighteen UCI datasets are used to evaluate the performance of the proposed approach. The results of the proposed method are compared with those of Particle Swarm Optimization (PSO), Genetic algorithms (GAs) in terms of classification accuracy and number of selected attributes. The results show the ability of Binary dragonfly algorithm (BDA) in searching the feature space and selecting the most informative features for classification tasks.

关键词： Feature Selection Optimization dragonfly algorithm classification

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Elite Opposition Learning and Exponential Function Steps-based dragonfly algorithm for Global Optimization

Elite Opposition Learning and Exponential Function Steps-bas...

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IEEE International Conference on Information and Automation (ICIA)

作者： Song, Junmin Li, Shangping Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning Peoples R China

ISBN: (纸本)9781538631546

dragonfly algorithm is a novel meta-heuristic optimization algorithm and was originally proposed for solving continuous optimization problems. To enhance the optimization performance of the dragonfly algorithm, this paper proposed an improved dragonfly algorithm, which was based on elite opposition-based learning strategy and exponential function adaptive steps. The elite individual is introduced to generate their opposite solutions by elite opposition-based learning. This mechanism can expand the scope of the search area and is helpful to improve the global exploration capability of this algorithm. At the same time, an adaptive step with exponential function is designed for replacing the original random step, so it can speed up the convergence rate of the algorithm. 15 typical benchmark test functions are applied to verify the effects of these improvements. The experimental results show that the proposed algorithm has faster convergence speed and higher convergence accuracy than basic dragonfly algorithm and sonic other meta heuristic optimization methods.

关键词： dragonfly algorithm elite opposition-based learning exponential function adaptive steps meta-heuristic

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