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Comparison of dragonfly algorithm and Harris hawks optimization evolutionary data mining techniques for the assessment of bearing capacity of footings over two-layer foundation soils

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ENGINEERING WITH COMPUTERS 2021年第1期37卷 437-447页

作者： Moayedi, Hossein Abdullahi, Mu'azu Mohammed Nguyen, Hoang Rashid, Ahmad Safuan A. Ton Duc Thang Univ Dept Management Sci & Technol Dev Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam Univ Hafr Al Batin Civil Engn Dept Hafar al Batin 39524 Eastern Provinc Saudi Arabia Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Univ Teknol Malaysia Fac Engn Sch Civil Engn Ctr Trop Geoengn Geotrop Johor Baharu Malaysia

By assist of novel evolutionary science, the classification accuracy of neural computing is improved in analyzing the bearing capacity of footings over two-layer foundation soils. To this end, Harris hawks optimization (HHO) and dragonfly algorithm (DA) are applied to a multi-layer perceptron (MLP) predictive tool for adjusting the connecting weights and biases in predicting the failure probability using seven settlement key factors, namely unit weight, friction angle, elastic modulus, dilation angle, Poisson's ratio, applied stress, and setback distance. As the first result, incorporating both HHO and DA metaheuristic algorithms resulted in higher efficiency of the MLP. Moreover, referring to the calculated area under the receiving operating characteristic curve (AUC), as well as the calculated mean square error, the DA-MLP (AUC = 0.942 and MSE = 0.1171) outperforms the HHO-MLP (AUC = 0.915 and MSE = 0.1350) and typical MLP (AUC = 0.890 and MSE = 0.1416). Furthermore, the DA surpassed the HHO in terms of time-effectiveness.

关键词： Bearing capacity analysis dragonfly algorithm Evolutionary science

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Heuristic dragonfly algorithm for optimal design of truss structures with discrete variables

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STRUCTURES 2021年 29卷 843-862页

作者： Jawad, Farqad K. J. Mahmood, Mohammed Wang, Dansheng AL-Azzawi, Osama Al-Jamely, Anas Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan Peoples R China Univ Diyala Dept Civil Engn Diyala Iraq Huazhong Univ Sci & Technol Sch Mech Engn Wuhan Peoples R China

The main purpose of this paper is to obtain more appropriate design for truss structures using a discrete novel nature-inspired optimization algorithm. The so-called dragonfly algorithm (DA) was employed to achieve the aim of this paper. The inspiration of the DA emerges from behaviors of static and dynamic swarming of dragonflies in the nature. The DA was originally developed for continuous optimization problems. This paper proposes some amendments to the DA for solving discrete functions of optimization problems and improving the performance of the algorithm. The modified algorithm was examined on five well-known planar and spatial steel trusses with discrete sizing variables. The effectiveness and robustness of the proposed algorithm were verified by comparing the new method with various widely-known metaheuristic algorithms. The numerical results demonstrate that the DA is more effective and accurate than other algorithms in terms of achieving the lightest weight with minimum number of structural analysis and satisfying the required structural constraints.

关键词： Truss optimization dragonfly algorithm Discrete variables Structural design Size optimization

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Prediction and application of porosity based on support vector regression model optimized by adaptive dragonfly algorithm

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2021年第9期43卷 1073-1086页

作者： Li, Zhongwei Xie, Yuqi Li, Xueqiang Zhao, Wenyang China Univ Petr Coll Comp & Commun Engn Qingdao 266580 Shandong Peoples R China

Porosity is an important parameter of reservoir physical properties and plays an important role in reservoir evaluation. Porosity can be measured by direct and indirect methods. However, most methods are very time-consuming and expensive. Therefore, it is of great significance to establish a fast, economical and effective method for accurate prediction of porosity. In this paper, an alternative method of porosity prediction, based on the integration between novel adaptive population heuristic intelligent optimization algorithm (ADA) and support vector regression (SVR) is presented. In this study, the simulation results of the new model were compared with the DA-SVR, BP, and ELM methods. The experimental results show that the ADA-SVR prediction model can achieve higher prediction accuracy and the prediction accuracy is 96.3%. Therefore, the proposed ADA-SVR model is feasible and effective for predicting porosity and can be used as an effective tool for predicting other reservoir parameters.

关键词： dragonfly algorithm adaptive strategy support vector regression porosity prediction geophysical well logs

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A New Set of Mutation Operators for dragonfly algorithm

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021年第9期46卷 8761-8802页

作者： Salgotra, Rohit Singh, Urvinder Singh, Supreet Singh, Gurdeep Saha, Sriparna Thapar Inst Engn & Technol Dept ECE Patiala Punjab India Indian Inst Technol Dept Comp Sci Patna Bihar India

dragonfly algorithm (DA) is a recently introduced, swarm intelligent algorithm and has proved its worth over real-world optimization problems. The algorithm is very efficient but is computationally expensive, has poor exploration properties, and unbalanced cohesion and alignment operation. In the present work, the concept of mutation operators has been exploited and seven new versions of DA have been proposed. Adaptive parameters, division of generations, improved exploitation phase and linearly decreasing population adaptation are also followed to improve the exploration, convergence and other properties of DA. The proposed algorithms are experimentally tested on CEC 2005 data set, CEC 2015 data set and CEC 2011 real-world benchmark problems and compared with respect to memory-based hybrid dragonfly algorithm (MHDA), hybrid memory-based dragonfly algorithm with differential evolution (DADE), sine-cosine crow search algorithm (SCCSA), success history-based adaptation differential evolution (SHADE), covariance matrix adaptation evolution strategy (CMA-ES), SHADE based on semi-parameter adaptation-based CMA-ES (LSHADE-SPACMA), hybrid particle swarm gravitational search algorithm (PSOGSA), hybrid firefly and particle swarm optimization algorithm (HFPSO) and others. Experimental and statistical results further validate that mutation clock-based DA (MDA) performs better than all other algorithms under comparison.

关键词： dragonfly algorithm Mutation operators Adaptive properties CEC benchmark problems Engineering design problems

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Optimized Distributed Cooperative Control for Islanded Microgrid Based on dragonfly algorithm

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ENERGIES 2023年第22期16卷 7675页

作者： Al-dulaimi, Falah Noori Saeed Kurnaz, Sefer Altmbas Univ Sch Engn & Nat Sci TR-34218 Istanbul Turkiye Middle Tech Univ Elect Engn Tech Coll Baghdad 10001 Iraq

This study introduces novel stochastic distributed cooperative control (SDCC) in the context of island microgrids (MGs). A proportional resonant (PR) controller and virtual impedance droop control in stationary reference frames are employed in cooperation with distributed averaging secondary control optimized by the dragonfly algorithm (DA). The suggested approach demonstrates the capability to achieve mean-square synchronization for the voltage and frequency restoration of distributed generators (DGs) to ensure efficient active power sharing. Therefore, a sparse communication network has been used to avoid data congestion and reduce the need for extensive communication and information exchange. The proposed system offers an instinctive compromise between voltage regulation and reactive power sharing. A conventional centralized secondary control with PR droop control is simulated for performance evaluation and comparison purposes. In this study, empirical evidence is demonstrated to support the MG's ability to confront communication failure and its ability to work reliably during plug-and-play operations.

关键词： microgrid cooperative control intelligent secondary dragonfly algorithm optimized distributed technique

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Parameter estimation of the wind speed distribution model by dragonfly algorithm

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2023年第3期38卷 1747-1756页

作者： Kose, Bayram Aygun, Hilmi Pak, Semih Izmir Bakircay Univ Dept Elect & Elect Engn TR-35665 Izmir Turkiye Karabuk Univ Dept Mechatron Engn TR-78050 Karabuk Turkiye Karabuk Univ Dept Elect & Elect Engn TR-78050 Karabuk Turkiye

Purpose: In modelling wind speed by Weibull probability distribution function (Wpdf) for potential calculation of wind energy and wind speed characterization, the purpose of the study is to estimate the distribution *** and Methods: The performance of the proposed method has been evaluated by comparing not only the classical methods which are the moment method (MM) and the least squares method (LSM) but also metaheuristic optimization algorithms which are dragonfly algorithm (DA), Genetic algorithm (GA) and Particle Swarm Optimization (PSO). Determination coefficient (R2) and root mean square error (RMSE) were used to evaluate the performance of these parameter estimation methods. Results: Dat obtaine from 6 measurement stations were used in the study. The results show that while the DA method gives the best performance according to the determination coefficient (R2) criterion in all stations, it provides the best performance in 2 stations according to the root mean square error (RMSE) criterion. In addition, it was observed that the DA method showed better performance in all stations compared to the LSM ***: It is seen that the proposed DA algorithm for wind energy potential calculation can be used to estimate Weibull and two-component mixed Weibull distribution parameters.

关键词： Weibull distribution parameters dragonfly algorithm Wind energy potential Estimation

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PMSM Vector Control Optimization Based on Fractional PI^(λ) of Rotational Speed Outer Loop of dragonfly algorithm

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Wuhan University Journal of Natural Sciences 2021年第5期26卷 429-436页

作者： ZENG Guohui FU Xiuwei LIU Jin HUANG Bo DU Tao ZHU Xiangchen School of Electronic and Electrical Engineering Shanghai University of Engineering ScienceShanghai 201620China

Aiming at the problems of slow dynamic response and weak robustness of integer-order proportional integral(PI)controller in double closed loop vector control system of permanent magnet synchronous motor(PMSM),a method of combining dragonfly algorithm with fractional order PI control is proposed for off-line parameter tuning for the outer loop of speed of the *** parameter to be optimized is used as the spatial position of the optimal individual searching for food sources in the search space,and the error performance index integrated time and absolute error(ITAE)is used as its target fitness *** motor speed regulation performances of traditional engineering experience setting integer order PI,particle swarm optimization algorithm tuning fractional order PI and dragonfly algorithm tuning fractional order PI are compared,*** show that the fractional order PI controller optimized by dragonfly algorithm can improve the dynamic response performance of the system,reduce overshoot and enhance robustness,which proves the feasibility and superiority of the optimization strategy.

关键词： permanent magnet synchronous motor dragonfly algorithm fractional-order PI control parameter offline tuning integrated time and absolute error(ITAE)

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Optimal sizing of solar-wind based hybrid energy system using modified dragonfly algorithm for an institution

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ENERGY CONVERSION AND MANAGEMENT 2023年第1期283卷

作者： George, D. X. Tittu Raj, R. Edwin Rajkumar, Ananth Mabel, M. Carolin St Xaviers Catholic Coll Engn Dept Mech Engn Nagercoil 629003 Tamil Nadu India Gati Shakti Vishwavidyalaya Dept Mech Engn Vadodara 390004 Gujarat India

Globally, most solar and wind power systems can produce power cheaper than fossil fuel-powered systems under certain circumstances according to the international renewable energy agency (IRENA). Despite that, the penetration rate of renewable energy has slowed down recently due to its non-availability and reliability in providing an uninterruptible power supply. A hybrid renewable energy system (HRES) with the conventional grid to counter the intermittency challenges without battery storage is a way forward for a greener solution. In this paper, unutilized rooftop space and underutilized potential locations of an educational institution are customized for renewable Solar-Wind power generation together with the conventional grid for effective and cost-optimal power solutions. A modified dragonfly algorithm (MDA) was adopted for mathematical modeling to reduce the power systems' net present value (NPV) over its lifespan. The model reckons various scenarios and found that the HRES with grid interaction is the best case to significantly improve the reliability economically. The result is encouraging to promote HRES in potential institutions to boost the growth of renewable energy, economically to achieve the net zero emission goal of IRENA by 2050.

关键词： Energy optimization Hybrid renewable energy system Solar-wind energy Reliability dragonfly algorithm

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A hybrid dragonfly algorithm for the vehicle routing problem with stochastic demands

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INTELLIGENT SYSTEMS WITH APPLICATIONS 2023年 18卷

作者： Marinaki, Magdalene Taxidou, Andromachi Marinakis, Yannis Tech Univ Crete Sch Prod Engn & Management Univ Campus Khania 73100 Greece

A number of swarm intelligence algorithms have been proposed during the last years. Most of them are suitable for the solution of continuous optimization problems. One of them is the dragonfly algorithm that has been proved very efficient in the problems in which it has been applied. However, few of the newly proposed algorithms have been used for the solution of a routing problem. In this paper, a new hybridized version of the dragonfly algorithm with the Combinatorial Expanding Neighborhood Topology is proposed and analyzed in details. The proposed Combinatorial Expanding Neighborhood Topology dragonfly algorithm is an algorithm that combines a very powerful swarm intelligence algorithm, the dragonfly algorithm, with a very effective procedure, the Combinatorial Expanding Neighborhood Topology. This algorithm was used for solving a well known routing problem, the Vehicle Routing Problem with Stochastic Demands. The algorithm was tested in 40 benchmark instances from the literature and gave, in some of them new, best solutions. It was, also, compared with 10 other swarm intelligence algorithms from the literature proving its effectiveness, as it was ranked in the first place among all the algorithms.

关键词： dragonfly algorithm Vehicle routing problem with stochastic demands Combinatorial expanding neighborhood topology Path relinking Variable neighborhood search

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An Improved dragonfly algorithm Based on Angle Modulation Mechanism for Solving 0-1 Knapsack Problems 12th

An Improved Dragonfly Algorithm Based on Angle Modulation Me...

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12th International Conference on Swarm Intelligence (ICSI)

作者： Wang, Lin Shi, Ronghua Li, Wenyu Yuan, Xia Dong, Jian Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China

ISBN: (纸本)9783030787424;9783030787431

dragonfly algorithm (DA) is a new intelligent algorithm based on the theory of dragonfly foraging and evading predators. DA exhibits excellent performance in solving multimodal continuous problems. To make DA work in the binary spaces, this paper introduces an angle modulation mechanism on DA (called AMDA) to generate bit strings, that is, to give alternative solutions to binary problems. Instead of running on the original high-dimensional binary spaces, the original AMDA utilizes the four-dimensional trigonometric function. However, the original AMDA has certain limitations, such as poor algorithm stability and slow convergence speed. Therefore, an improved AMDA called IAMDA is proposed. Based on the original generating function, a variable coefficient is added to control the vertical displacement of the cosine function. In this paper, seven high-dimensional zero-one knapsack problems are considered. Experimental results prove that IAMDA has superior convergence speed and quality of solution as compared to AMDA, BDA and BPSO.

关键词： Angle modulation mechanism Trigonometric generating function dragonfly algorithm Binary optimization 0-1 knapsack problem

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