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Vehicle power train optimization using multi-objective bird swarm algorithm

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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Modelling of climatic change effects on runoff values using bird swarm algorithm: case study - Seine basin (France)

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INTERNATIONAL JOURNAL OF HYDROLOGY SCIENCE AND TECHNOLOGY 2022年第4期14卷 359-374页

作者： Naoui, Noureddine Oulad Cherif, El-Amine Djehiche, Abdelkader Sekkoum, Mohamed Univ Amine Elokkal Elhaj Moussa EgAkhamok Dept Sci & Technol Mat & Energies Res Lab POB 10034 Tamanrasset Algeria Univ Sci & Technol Oran Mohamed Boudiaf BP 1505 El MNaouer Oran Algeria Univ Ammar Telidji Laghouat BP G37Rd Ghardaia Mkam 03000 Algeria

The primary focus of this paper is the evapotranspiration (ET)-runoff modelling on an annual scale in the Seine basin. In this study, bird swarm algorithm (BSA) is used to estimate mathematical influence of temperature on runoff values. We employ the rural engineering model (GR1) for annual timestep. We calibrate GR1 model with the ET values using BSA, determining the model parameter sets that best reflect the transformation of evapotranspiration into flow. The calibration was done with Matlab software in a computer. Statistical parameters values give acceptable runoff values for all climatic conditions modelled by BSA-high values of climatic condition (BSA-HVCC = 0.56) and low values of climatic condition (BSA-LVCC = 0.82). Variations caused by climatic change are translated by the variation coefficient (V.C1 = 0.67 for high values of climatic condition and V.C2 = 0.99 for low values of climatic condition), which is the main objective of our work.

关键词： climatic change modelling social behaviours bird swarm algorithm BSA temperature-runoff hydrology France

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Multiple sequence alignment using enhanced bird swarm align algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第1期41卷 1097-1114页

作者： Rehman, Hafiz Asadul Zafar, Kashif Khan, Ayesha Imtiaz, Abdullah Natl Univ Comp & Emerging Sci Lahore Dept Comp Sci Lahore Pakistan Univ Management & Technol Lahore Pakistan Fordham Univ New York NY 10023 USA

Discovering structural, functional and evolutionary information in biological sequences have been considered as a core research area in Bioinformatics. Multiple Sequence Alignment (MSA) tries to align all sequences in a given query set to provide us ease in annotation of new sequences. Traditional methods to find the optimal alignment are computationally expensive in real time. This research presents an enhanced version of bird swarm algorithm (BSA), based on bio inspired optimization. Enhanced bird swarm Align algorithm (EBSAA) is proposed for multiple sequence alignment problem to determine the optimal alignment among different sequences. Twenty-one different datasets have been used in order to compare performance of EBSAA with Genetic algorithm (GA) and Particle swarm Align algorithm (PSAA). The proposed technique results in better alignment as compared to GA and PSAA in most of the cases.

关键词： Multiple sequence alignment Particle swarm optimization Bioinformatics Genetic algorithm swarm intelligence bird swarm algorithm

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Henry gas bird swarm optimization algorithm-based deep learning for brain tumor classification using magnetic resonance imaging

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第4期35卷 e7541-e7541页

作者： Omana, Sinciya Ponnupilla Dar, Jawad Ahmad Kumar, Thevasigamani Rajesh Sampath, Arpakkam Karuppan Sharma, Sudhir Amal Jyothi Coll Engn Dept Comp Sci & Engn Kanjirapally India Mansarovar Global Univ sehore Dept Comp Sci & Engn sehore Madhya Pradesh India Saveetha Univ Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Comp Sci & Engn Chennai India Presidency Univ CSE Bangalore India Manipal Univ Jaipur Sch Comp & Informat Technol Dept Informat Technol Rajasthan India

In order to categorize brain tumors, this study uses a deep convolutional neural network (DCNN) based on Henry gas bird swarm optimization (HGBSO). The Henry gas solubility optimization (HGSO) and bird swarm algorithm techniques were merged to form the HGBSO algorithm, which was used to train the DCNN classifier. Following the first preprocessing of the images using the Gaussian filter, the Region of Interest extraction approach is utilized to reduce noise from the input MR images. After that, the regions of brain tumors are divided using a deep fuzzy clustering technique. Meanwhile, key characteristics are taken from the segmented image in order to perform an efficient classification procedure. Moreover, for improving the classification accuracy rate, data augmentation is performed. Finally, the augmented data along with total features are considered as input for developed HGBSO trained DCNN classifier, where the classification of brain tumor is performed. In terms of various metrics, the developed strategy performs better than other existing methods, obtaining values of 0.9221 for accuracy, 0.9324 for sensitivity, and 0.9295 for specificity.

关键词： bird swarm algorithm brain tumor classification deep convolutional neural network Gaussian filter Henry gas solubility optimization technique

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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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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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Modified bird swarm algorithm for edge detection in noisy images using fuzzy reasoning

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2019年第4期7卷 450-463页

作者： Pruthi, Jyotika Arora, Shaveta Khanna, Kavita NorthCap Univ Dept Comp Sci & Engn Gurgaon 122017 Haryana India NorthCap Univ Dept Elect & Commun Engn Elect Gurgaon India

A new bio-inspired edge detection approach is proposed to deal with the noisy images using a combination of bird swarm algorithm (BSA) and fuzzy reasoning. BSA is based on the behaviour of the birds. The birds fly through each pixel while they forage for the food and detect the edge pixels and noisy pixels that fall in their path. The direction in which the birds fly is found using fuzzy rule-based system. The pixels are classified as edge and non-edge pixels using the concept of thresholding. The noisy pixels are removed by the birds using fuzzy impulse noise detection and reduction method. The technique has been evaluated on two standard image datasets for quantitative and qualitative analysis. The results clearly indicate significant improvement over the other bio-inspired approaches, namely, Genetic algorithm (GA), Particle swarm Optimisation (PSO), Ant Colony Optimisation (ACO), Bacterial Foraging algorithm (BFO), Deep Learning approach, fuzzy with BFO for noisy images, Neuro-fuzzy approach and PSO particularly for images having impulse noise in terms of Entropy, Kappa Value, Pratt's Figure of Merit (FoM) and Structural Similarity Index Measure (SSIM). The proposed approach works well to detect the continuous, thin and smooth edges in the presence of 5-40% noise density.

关键词： bird swarm algorithm edge detection impulse noise fuzzy reasoning

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Application of bird swarm algorithm for Solution of Optimal Power Flow Problems 12th

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12th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS)

作者： Ahmad, Manzoor Javaid, Nadeem Niaz, Iftikhar Azim Shafiq, Sundus Rehman, Obaid Ur Hussain, Hafiz Majid COMSATS Inst Informat Technol Islamabad 44000 Pakistan CASE Islamabad 44000 Pakistan

ISBN: (纸本)9783319936598;9783319936581

Incorporating fluctuant and intermittent nature wind power and solar photovoltaic (PV) power in the power system lead to significant challenges for system planning and operation which are risen from uncertainties associated with renewable energy. This paper placed emphasis on OPF problem. bird swarm algorithm (BSA) is employed to optimize power generation cost in power system network with handling the uncertainty of both wind power and solar PV power. To examine the effectiveness and accuracy of the BSA, the modified IEEE 30-bus system with two traditional thermal generators (TGs), two windfarms and two solar PV units is utilized.

关键词： bird swarm algorithm Optimal Power Flow Wind power Solar PV power Uncertainty

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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年第Jul.期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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Variable Strength Combinatorial Test Data Generation Using Enhanced bird swarm algorithm 19

Variable Strength Combinatorial Test Data Generation Using E...

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19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)

作者： Cai, Lizhi Zhang, Yang Ji, Weijia East China Univ Sci & Technol Sch Informat Sci & Engineer Shanghai Peoples R China Shanghai Dev Ctr Comp Software Technol Lab Comp Software Testing & Evaluating Shanghai Peoples R China

ISBN: (纸本)9781538658895

combinatorial testing is an effective black box testing technique for the system with large numbers of parameters and their values. However, for significantly complex and key systems, combinatorial testing still owns high complexity. Testing completely the core part of these systems usually is a kind of solution and Variable strength combinatorial test data generation (VS-CTDG) emerges. In this paper, enhanced bird swarm algorithm (EBSA), a variant of bird swarm algorithm (BSA), is employed into the problem. Moreover, a testing requirement reduction is proposed and makes EBSA more suitable into VS-CTDG than ever. Through benchmarks, EBSA is proved an effective approach.

关键词： search-based software testing variable strength bird swarm algorithm combinatorial testing

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