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An Orthogonal Learning bird swarm algorithm for Optimal Power Flow Problems

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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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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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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Modified bird swarm algorithm for blind image steganalysis

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International Journal of Information Technology (Singapore) 2023年第6期15卷 2877-2888页

作者： Chhikara, Rita Sharma, Prabha Chandra, B. Malik, Nidhi SOET The Northcap University Gurugram India IT School IIT Delhi New Delhi India

Blind image Steganalysis is gaining lot of importance these days owing to its application in cyberwarfare, computer forensics, tracking anti-social elements over internet. In this paper a modified discrete bird swarm algorithm (DBSA) referred as Dy_DBSA has been proposed for feature selection. The proposed algorithm not only reduces computational cost in blind image steganalysis but also increases classification accuracy. The algorithm is designed by dynamically modifying cognitive, social and flight parameters of bird swarm algorithm and uses modified discretization function to achieve good classification accuracy for steganalysis problem. A hybrid Dy_DBSA is designed by combining Dy_DBSA with filter approaches in order to reduce computational complexity. Unlike DBSA the Dy_DBSA algorithm does not get stuck in a local optimum. Apart from validating the proposed model on datasets generated by images from different sites, it has also been tested on benchmark HUGO (Highly Undetectable steGO) dataset. The comparative performance evaluation has shown that the proposed algorithm outperforms the state-of-art feature selection algorithms. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： bird swarm algorithm Feature selection Filter approach Steganalysis Steganography Support vector machine Wrapper approach

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Application of adaptive mutation bird swarm algorithm in optimal dispatching of microgrid grid connection

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2022年第6期22卷 2279-2293页

作者： Wang, Chen Rong, Xiao Dou, Zhenhai Liu, Yawei Shao, Kaiguang Liu, Lianxin Shandong Univ Technol Sch Elect & Elect Engn Zibo 255000 Shandong Peoples R China State Grid Shandong Elect Power Co Liaocheng Power Supply Co Liaocheng Shandong Peoples R China Tech Coll Branch State Grid Corp China Jinan Shandong Peoples R China China Petrochem Corp Shengli Petr Adm Co Ltd Elect Branch Dongyin Shandong Peoples R China

Distributed power grid integration contributes to both the reduction of greenhouse gas emissions and the protection of the environment. Nevertheless, the uncertainty and volatility associated with the production of clean renewable energy adds additional challenges to microgrid dispatch. The paper presents an adaptive mutant bird swarm algorithm and suggests a comparison mechanism based on population fitness variances and optimal values in order to overcome the shortcomings of BSA, in particular its tendency to self-correct into local optimum and slow convergence speed. First, the algorithm determines if the population is in the local optimal state. The local optimal individual is then subjected to Cauchy mutation in order to determine the optimal value again. This improves the accuracy and speed of the BSA. Based on simulation results, the improved algorithm has higher optimization accuracy and faster optimization speed, which demonstrates the effectiveness and advancement of the algorithm proposed in this research.

关键词： Micro grid optimal scheduling bird swarm algorithm adaptive mutation distributed power

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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...

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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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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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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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Vehicle Feature Recognition via A Convolutional Neural Network with An Improved bird swarm algorithm

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JOURNAL OF INTERNET TECHNOLOGY 2023年第2期24卷 421-432页

作者： Chen, Xuan Zhejiang Ind Polytech Coll Design & Art Branch Shaoxing Peoples R China

Accurate vehicle feature recognition is an important element in traffic intelligence systems. To address the problems of slow convergence and weak generalization ability in using convolutional neural networks to improve vehicle feature recognition, we propose an improved bird swarm algorithm to optimize convolutional neural networks (IBSA-CNNs) for vehicle recognition strategies. First, we use the center of gravity backward learning strategy and similarity-and aggregation-based optimization strategy in population initialization and foraging behavior, respectively, to improve the algorithm performance and avoid falling into a local optimum. Second, the improved bird swarm algorithm is used to optimize the weights of the convolutional and pooling layers of the convolutional neural network to improve the neural network performance. Finally, we tested the performance of the improved bird swarm algorithm in simulation experiments using benchmark functions. The recognition performance of IBSA-CNN was tested by the UCI dataset, and in the traffic vehicle dataset BIT-Vehicle, it improved 4.9% and 6.8% compared with R-CNN and CNN, respectively, indicating that IBSA-CNN has better vehicle feature recognition.

关键词： bird swarm algorithm Convolutional neural network Feature recognition

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Torsional capacity evaluation of RC beams using an improved bird swarm algorithm optimised 2D convolutional neural network

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ENGINEERING STRUCTURES 2022年 273卷 1页

作者： Yu, Yang Liang, Shiwei Samali, Bijan Nguyen, Thuc N. Zhai, Chenxi Li, Jianchun Xie, Xingyang Western Sydney Univ Ctr Infrastruct Engn Penrith NSW 2747 Australia Taizhou Customs Comprehens Tech Serv Ctr Taizhou 318000 Zhejiang Peoples R China Univ Technol Sydney Sch Civil & Environm Engn Ultimo NSW 2007 Australia Cornell Univ Sibley Sch Mech & Aerosp Engn Ithaca NY 14853 USA

This study presents the application of deep learning technology in torsional capacity evaluation of reinforced concrete (RC) beams. A data-driven model based on 2D convolutional neural network (CNN) is established, where model inputs contain the beam width, beam height, stirrup width, stirrup height, concrete compressive strength, steel ratio of longitudinal reinforcement, yield strength of longitudinal reinforcement, steel ratio of transverse reinforcement, yield strength of transverse reinforcement and stirrup spacing. To enhance the pre-diction accuracy of the proposed model, an improved bird swarm algorithm (IBSA) is leveraged to optimise the hyperparameters of CNN in the training phase. A comprehensive dataset, comprising 268 groups of laboratory tests of RC beams collected from published articles, is used for model development and validation. The results show that the proposed 2D CNN with hyperparameter optimisation exhibits high performance in predicting torsional strength of RC beams, which outperforms other machine learning models, building codes and empirical formula in terms of a series of evaluation metrics.

关键词： Torsional strength Reinforced concrete (RC) beams Convolutional neural network bird swarm algorithm

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