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bird swarm algorithm Applied to the Wide-Area Damping Controller Design

Bird Swarm Algorithm Applied to the Wide-Area Damping Contro...

IEEE IFAC International Conference on Automation (ICA) / 24th Congress of the Chilean-Association-of-Automatic-Control (ACCA)

作者： Bento, Murilo E. C. Univ Sao Paulo Dept Elect & Comp Engn BR-13566590 Sao Carlos SP Brazil

ISBN: (纸本)9781665401272

Low-frequency oscillation modes, local or interareas, when not properly mitigated can compromise the stability of power system operation and consequently the supply of electricity to consumer centers. Traditionally, the use of Power System Stabilizers has been promising in mitigating local oscillation modes but has limited effect in inter-area modes. Recent advances in communication technologies have enabled the creation of Wide-Area Damping Controllers (WADCs) whose input signals are provided by Phasor Measurement Units (PMUs). These WADC is multivariable and thus is more promising in mitigating inter-area oscillation modes. However, variable delays in the transmission of signals on the communication channels can compromise the design. This article proposes a WADC design method based on bird swarm algorithm considering two limits for signal transmission delays: one stipulated for the normal operation of the control system and the other for cases where the time delay exceeded the first limiter for some reason. Case studies are presented in this article including statistical evaluation and nonlinear time-domain simulations.

关键词： Power Systems Low-Frequency Oscillation Modes Wide-Area Damping Controller Small-Signal Stability Time Delay bird swarm algorithm

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Vehicle power train optimization using multi-objective bird swarm algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第21-22期79卷 14319-14339页

作者： Wu, Dongmei Pun, Chi-Man Xu, Bin Gao, Hao Wu, Zhenghua Nanjing Univ Posts & Telecommun Sch Automat Nanjing Peoples R China Nanjing Univ Posts & Telecommun Nanjing Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China

In this paper, a multi-objective bird swarm algorithm (MOBSA) is proposed to cope with multi-objective optimization problems. The algorithm is explored based on BSA which is an evolutionary algorithm suitable for single objective optimization. In this paper, non-dominated sorting approach is used to distinguish optimal solutions and parallel coordinates is applied to evaluate the distribution density of non-dominated solution and further update the external archive when it is full to overflowing, which ensure faster convergence and more widespread of Pareto front. Then, the MOBSA is adopted to optimize benchmark problems. The results demonstrate that MOBSA gets better performance compared with NSGA-II and MOPSO. Since a vehicle power train problem could be treated as a typical multi-objective optimization problem with constraints, with integration of constrained non-dominated solution, MOBSA is adopted to acquire optimal gear ratios and optimize vehicle power train. The results compared with other popular algorithm prove the proposed algorithm is more suitable for constrained multi-objective optimization problem in engineering field.

关键词： bird swarm algorithm MOBSA Multi-objective optimization Constraint Vehicle power train optimization

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Solving Multi-Objective Problems Using bird swarm algorithm

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IEEE ACCESS 2021年 9卷 36382-36398页

作者： Houssein, Essam H. Ahmed, Mohammed M. Abd Elaziz, Mohamed Ewees, Ahmed A. Ghoniem, Rania M. Menia Univ Fac Comp & Informat Al Minya 61519 Egypt Univ Sadat City Fac Comp & Artificial Intelligence Sadat City 32897 Egypt Zagazig Univ Fac Sci Zagazig 44511 Egypt Damietta Univ Dept Comp Dumyat 34511 Egypt Princess Nourah bint Abdulrahman Univ Dept Informat Technol Coll Comp & Informat Sci Riyadh 84428 Saudi Arabia Mansoura Univ Dept Comp Mansoura 35516 Egypt

This paper introduces an effective method by combining the multi-objective technique with the bird swarm algorithm (BSA) to obtain a new method called MBSA. The MBSA obtains some of the different non-dominated techniques that maintain variety amongst the optimal solutions. To verify and evaluate the effectiveness of the MBSA, collections of constrained, unconstrained, and engineering problems are measured. These problems have various Pareto front (PF) properties, including non-convex, convex, and discrete PFs. The results show that the MBSA has a good ability to obtain both a better solution spread and better convergence near the true PF. Furthermore, the quantitative and qualitative results indicate that the MBSA provides high convergence and good results in all experiments and with real-world problems against well-known algorithms in the literature.

关键词： Optimization birds Convergence Computers Transportation Sorting Search problems bird swarm algorithm multi-objective optimization constrained optimization problems unconstrained optimization problems engineering optimization problems Pareto front Pareto optimal solutions

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Edge-based target detection for unmanned aerial vehicles using competitive bird swarm algorithm

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AEROSPACE SCIENCE AND TECHNOLOGY 2018年 78卷 708-720页

作者： Wang, Xiaohua Deng, Yimin Duan, Haibin Beihang Univ State Key Lab Virtual Real Technol & Syst Sch Automat Sci & Elect Engn Beijing 100083 Peoples R China

Target detection for unmanned aerial vehicles is an important issue in autonomous formation flight. In this paper, a novel target detection approach for unmanned aerial vehicle formation is proposed based on edge matching. The windowed edge potential function is utilized to describe the attraction field for similar edges. Afterwards, the edge-based target detection problem can be formulated as an optimization problem. An improved version of the bird swarm algorithm, which is called competitive bird swarm algorithm, is proposed to find the location, rotation angle and scale of a given template on a specific image. A strategy named "disturbing the local optimum" is designed to help the original bird swarm algorithm converge to the global optimal solution faster and more stably. Formation flight platforms, which consists of unmanned aerial vehicles moving in leader-follower pattern, are used in our experiments. Images obtained by vision sensors embedded in the leaders are used to verify the effectiveness of the proposed method. The proposed algorithm is tested on both indoor and outdoor images to demonstrate the robustness. Comparative experiments with other state-of-the-art algorithms, including genetic algorithm, particle swarm optimization, artificial bee colony algorithm, pigeon-inspired optimization, and the basic bird swarm algorithm, are also conducted. The results prove the superiority and robustness of the proposed target detection algorithm. (C) 2018 Elsevier Masson SAS. All rights reserved.

关键词： Unmanned aerial vehicle Target detection Edge potential function bird swarm algorithm

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Forecasting Short-Term Wind Speed Based on IEWT-LSSVM model Optimized by bird swarm algorithm

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IEEE ACCESS 2019年 7卷 59333-59345页

作者： Xiang, Ling Deng, Zeqi Hu, Aijun North China Elect Power Univ Mech Engn Dept Baoding 071003 Peoples R China

Accurate wind speed prediction can improve the utilization efficiency of wind energy effectively. However, the original time series signals of wind speed present nonlinear or non-stationary characteristics, which make wind speed forecasting very difficult. A new pre-processing method of wind speed signal, named improved empirical wavelet transform (IEWT), is proposed in this paper, which is inspired by the spectrum segmentation theory of EWT. Then, a novel hybrid model combining IEWT and least square support vector machine (LSSVM) is put forward to predict short-term wind speed. In order to achieve better prediction results, the parameters are jointly optimized through bird swarm algorithm (BSA) in this model. The prediction performance of the established hybrid model is tested with practical wind speed signal of four seasons from a wind farm situated in Zhejiang, China. The experimental results indicate that the presented hybrid forecasting model can effectively follow the change of wind speed, which exhibits more superior predicting performance than other popular models.

关键词： Wind speed forecasting improved empirical wavelet transform least square support vector machine bird swarm algorithm

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Multi-objective microgrid optimal dispatching based on improved bird swarm algorithm

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Global Energy Interconnection 2022年第2期5卷 154-167页

作者： Xiaoyan Ma Yunfei Mu Yu Zhang Chenxi Zang Shurong Li Xinyang Jiang Meng Cui Key Laboratory of Smart Grid of Ministry of Education Tianjin UniversityTianjin 300072P.R.China Global Energy Interconnection Development and Cooperation Organization Beijing 100031P.R.China Xiamen University Xiamen 361102P.R.China State Grid Xiongan New Area Electric Power Supply Company Baoding 071700P.R.China State Grid Baoding Electric Power Supply Company Baoding 071000P.R.China

Multi-objective optimal dispatching schemes with intelligent algorithms are recognized as effective measures to promote the economics and environmental friendliness of microgrid ***,the low accuracy and poor convergence of these algorithms have been challenging for system *** bird swarm algorithm(BSA),a new bio-heuristic cluster intelligent algorithm,can potentially address these challenges;however,its computational iterative process may fall into a local optimum and result in premature convergence when optimizing small portions of multi-extremum *** analyze the impact of a multi-objective economic-environmental dispatching of a microgrid and overcome the aforementioned problems of the BSA,a self-adaptive levy flight strategy-based BSA(LF-BSA)was *** can solve the dispatching problems of microgrid and enhance its dispatching convergence accuracy,stability,and speed,thereby improving its optimization *** typical test functions were used to compare the LF-BSA with three commonly accepted algorithms to verify its ***,a typical summer-time daily microgrid scenario under grid-connected operational conditions was *** results proved the feasibility of the proposed LF-BSA,effectiveness of the multi-objective optimization,and necessity of using renewable energy and energy storage in microgrid dispatching optimization.

关键词： Microgrid Operation optimization bird swarm algorithm Levy flight strategy Self-adaptive

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Evolving neural networks using bird swarm algorithm for data classification and regression applications

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第4期22卷 1317-1345页

作者： Aljarah, Ibrahim Faris, Hossam Mirjalili, Seyedali Al-Madi, Nailah Sheta, Alaa Mafarja, Majdi Univ Jordan King Abdullah II Sch Informat Technol Dept Informat Technol Amman Jordan Griffith Univ Sch Informat & Commun Technol Brisbane Qld 4111 Australia Princess Sumaya Univ Technol King Hussein Fac Comp Sci Amman Jordan Texas A&M Univ Dept Comp Sci Corpus Christi TX 78412 USA Birzeit Univ Dept Comp Sci Birzeit Palestine

This work proposes a new evolutionary multilayer perceptron neural networks using the recently proposed bird swarm algorithm. The problem of finding the optimal connection weights and neuron biases is first formulated as a minimization problem with mean square error as the objective function. The BSA is then used to estimate the global optimum for this problem. A comprehensive comparative study is conducted using 13 classification datasets, three function approximation datasets, and one real-world case study (Tennessee Eastman chemical reactor problem) to benchmark the performance of the proposed evolutionary neural network. The results are compared with well-regarded conventional and evolutionary trainers and show that the proposed method provides very competitive results. The paper also considers a deep analysis of the results, revealing the flexibility, robustness, and reliability of the proposed trainer when applied to different datasets.

关键词： Optimization Neural networks Multilayer perceptron bird swarm algorithm Classification Regression

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An Orthogonal Learning bird swarm algorithm for Optimal Power Flow Problems

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IEEE ACCESS 2023年 11卷 23659-23680页

作者： Ahmad, Manzoor Javaid, Nadeem Niaz, Iftikhar Azim Ahmed, Ijaz Hashmi, Muhammad Adnan COMSATS Univ Islamabad Dept Comp Sci Vehari Campus Vehari 61100 Pakistan COMSATS Univ Islamabad Dept Comp Sci Islamabad 44000 Pakistan Univ Technol & Appl Sci Dept Informat Technol Ibri Campus Ibri 516 Oman Abu Dhabi Mens Campus Higher Coll Technol Fac Comp Informat Sci Abu Dhabi U Arab Emirates

A dominant statistical method, in which the best combination of factors' levels are predicted by analyzing a few representative combinations of factors' levels, named as orthogonal experimental design (OED). The OED is an effective approach for analyzing the effect of multi-levels factors simultaneously and it works on orthogonal learning (OL) strategy. An evolutionary programming based heuristic method has two contradictory features-exploration and exploitation, balancing in these features have significant impact on its optimization performance. We have applied an OED based auxiliary search strategy for enhancing performance of the bird swarm algorithm (BSA) by improving its exploitation search ability. It is a challenging task to keep balance among two contradictory features-exploration and exploitation of a heuristic approach, while addressing optimal power flow (OPF) problems in power systems. In this research study, we have proposed improved BSA (IBSA) for solving the OPF problems in thermal power systems. We have conducted a study of the OPF problems with objective functions-reducing electricity generation cost, emission pollution, and active power loss to measure the efficiency of proposed IBSA. In this work, we have utilized five benchmark functions and solved OPF problems using three IEEE test systems including IEEE-30 bus system, IEEE-57 bus system, and IEEE-118 bus system to verify stability, effectiveness, and performance of proposed IBSA. The statistical and simulation results have indicated that the proposed IBSA has better convergence, efficiency, and robustness features than the original BSA as well as other heuristic approaches. It is observed that lowest electricity generation cost 800.3975 $/h on IEEE-30 bus system, 41663.5500$ /h on IEEE-57 bus system, and 134941.0367 $ /h on IEEE-118 bus system have been achieved using proposed IBSA to address the OPF problems. Furthermore, in transmission lines of the power system network minimum active power l

关键词： Search problems Heuristic algorithms Metaheuristics Power system stability Learning systems Costs Smart grids Orthogonal learning bird swarm algorithm optimal power flow smart power grid

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The Recognition Method of MQAM Signals Based on BP Neural Network and bird swarm algorithm

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IEEE ACCESS 2021年 9卷 36078-36086页

作者： Zhang, Chengchang Yu, Sa Li, Guojun Xu, Yu Chongqing Univ Posts & Telecommun Inst Optoelect Engn Chongqing 400065 Peoples R China

With the commercialization of 5G, in order to recognize QAM signals, one of the main modulation modes in 5G communication systems, this paper put forward the BP-BSA network model based on bird swarm algorithm (BSA) and BP neural network. Firstly, the instantaneous features and high-order cumulants were selected as the appropriate feature statistics by analyzing the characteristics of MQAM signals. After that, the structure of the BP neural network model was determined, and the initial parameters of the BP neural network were optimized using BSA to accelerate the convergence speed of the network. Finally, the features processed with signal to noise ratio (SNR) disorder were used to train and test the BP neural network model. The BP-BSA network model proposed in this paper applies the bird swarm algorithm to the field of modulation recognition for the first time. And the simulation results show that the recognition accuracies of 16, 32, 64, 128, 256QAM signals in the SNR range of -5 dB to 20 dB all reach above 98%. Compared with the same type algorithms, the algorithm proposed in this paper has good recognition performance.

关键词： Quadrature amplitude modulation Neural networks Modulation Feature extraction Signal to noise ratio birds Training MQAM signals recognition BP neural network bird swarm algorithm feature extraction

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Construction cost prediction system based on Random Forest optimized by the bird swarm algorithm

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第8期20卷 15044-15074页

作者： Zheng, Zhishan Zhou, Lin Wu, Han Zhou, Lihong Jiangxi Univ Engn Sch Big Data & Comp Xinyu 338000 Peoples R China Jiangxi Univ Engn Sch Civil Engn Xinyu 338000 Peoples R China Nanchang Univ Sch Infrastruct Engn Nanchang 330047 Peoples R China Nanchang Inst Sci & Technol Sch Architecture & Environm Engn Nanchang 330047 Peoples R China

Predicting construction costs often involves disadvantages, such as low prediction accuracy, poor promotion value and unfavorable efficiency, owing to the complex composition of construction projects, a large number of personnel, long working periods and high levels of uncertainty. To address these concerns, a prediction index system and a prediction model were developed. First, the factors influencing construction cost were first identified, a prediction index system including 14 secondary indexes was constructed and the methods of obtaining data were presented elaborately. A prediction model based on the Random Forest (RF) algorithm was then constructed. bird swarm algorithm (BSA) was used to optimize RF parameters and thereby avoid the effect of the random selection of RF parameters on prediction accuracy. Finally, the engineering data of a construction company in Xinyu, China were selected as a case study. The case study showed that the maximum relative error of the proposed model was only 1.24%, which met the requirements of engineering practice. For the selected cases, the minimum prediction index system that met the requirement of prediction accuracy included 11 secondary indexes. Compared with classical metaheuristic optimization algorithms (Particle swarm Optimization, Genetic algorithms, Tabu Search, Simulated Annealing, Ant Colony Optimization, Differential Evolution and Artificial Fish School), BSA could more quickly determine the optimal combination of calculation parameters, on average. Compared with the classical and latest forecasting methods (Back Propagation Neural Network, Support Vector Machines, Stacked Auto-Encoders and Extreme Learning Machine), the proposed model exhibited higher forecasting accuracy and efficiency. The prediction model proposed in this study could better support the prediction of construction cost, and the prediction results provided a basis for optimizing the cost management of construction projects.

关键词： building engineering construction cost prediction Random Forest bird swarm algorithm

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