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Evolving Artificial Neural Networks Using butterfly optimization algorithm for Data Classification 26th

Evolving Artificial Neural Networks Using Butterfly Optimiza...

26th International Conference on Neural Information Processing (ICONIP) of the Asia-Pacific-Neural-Network-Society (APNNS)

作者： Jalali, Seyed Mohammad Jafar Ahmadian, Sajad Kebria, Parham M. Khosravi, Abbas Lim, Chee Peng Nahavandi, Saeid Deakin Univ Inst Intelligent Syst Res & Innovat IISRI Geelong Vic Australia Kermanshah Univ Technol Kermanshah Iran

ISBN: (纸本)9783030367084;9783030367077

One of the most difficult challenges in machine learning is the training process of artificial neural networks, which is mainly concerned with determining the best set of weights and biases. Gradient descent techniques are known as the most popular training algorithms. However, they are susceptible to local optima and slow convergence in training. Therefore, several stochastic optimization algorithms have been proposed in the literature to alleviate the shortcomings of gradient descent approaches. The butterfly optimization algorithm (BOA) is a recently proposed meta-heuristic approach. Its inspiration is based on the food foraging behavior of butterflies in the nature. Moreover, it has been shown that BOA is effective in undertaking a wide range of optimization problems and attaining the global optima solutions. In this paper, a new classification method based on the combination of artificial neural networks and BOA algorithm is proposed. To this end, BOA is applied as a new training strategy by optimizing the weights and biases of artificial neural networks. This leads to improving the convergence speed and also reducing the risk of falling into local optima. The proposed classification method is compared with other state-of-the-art methods based on two well-known data sets and different evaluation measures. The experimental results ascertain the superiority of the proposed method in comparison with the other methods.

关键词： butterfly optimization algorithm Artificial neural network Classification Meta-heuristic

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Underground Face Drill Jumbos Path-planning Based on Improved butterfly optimization algorithm 43

Underground Face Drill Jumbos Path-planning Based on Improve...

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43rd Chinese Control Conference (CCC)

作者： Wang, Yuning Liu, Jian Zhang, Lei Sun, Hao Hebei Univ Technol Coll Artif Intelligence Tianjin 300130 Peoples R China Hebei Univ Technol Control Engn Technol Innovat Ctr Hebei Prov Tianjin 300401 Peoples R China Zhangjiakou Xuanhua Huatai Min Met Machinery Co L Zhangjiakou Peoples R China

ISBN: (纸本)9798350366907;9789887581581

In this paper, a butterfly optimization algorithm is used for path planning of autonomous driving of underground trackless face drill jumbos. On account of the shortcomings of the traditional butterfly optimization algorithm whose parameters are complex and fixed in view of the more severe and complex environment of the actual underground mine path, a method based on adaptive adjustment of the power exponent parameters is proposed to improve it. The algorithm is applied to improve the search accuracy and iterative capability as opposed to the traditional butterfly optimization algorithm, which is verified by comparing the fitness function with the planning simulation of different simulation maps.

关键词： butterfly optimization algorithm Path planning Adaptive control

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A Traditional butterfly optimization algorithm MPPT Method 2nd

A Traditional Butterfly Optimization Algorithm MPPT Method

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2nd International Conference on Advanced Intelligent Technologies (ICAIT)

作者： Zheng, Xubin Li, Mengda Liang, Zhichao Yao, Linping Shanghai DianJi Univ Sch Elect Engn Shanghai 201306 Peoples R China

ISBN: (纸本)9789811697357;9789811697340

After photovoltaic power generation for a period of time, the surface of photovoltaic array will be covered by stains or dust, leading to the phenomenon of multi-peak power characteristics of photovoltaic output. Under local shading conditions, the traditional MPPT algorithm cannot find the maximum power point out of local optimum. Therefore, a MPPT algorithm based on butterfly optimization is proposed. The simulation results show that the algorithm has a strong global optimization effect. The tracking accuracy of the traditional butterfly optimized MPPT algorithm is up to 98.25%, which is 37.64% higher than the maximum power tracking of the traditional perturbation observation method.

关键词： butterfly optimization algorithm Maximum power tracking Partial shade Photovoltaic power generation

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Load Balancing in Cloud Computing Environment using butterfly optimization algorithm 3

Load Balancing in Cloud Computing Environment using Butterfl...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Alzubaidi, Laith H. Arabi, Mohammed Yousif Kumar, Bura Vijay Kumar, M. Hema Almoussawi, Zainab Abed The Islamic University Najaf Iraq National University of Science and Technology Dhi Qar Iraq School of Computer Science and Artificial Intelligence SR University Telangana Warangal India Electronics and Communication Engineering Sona College of Technology Salem India College of Islamic Science Ahl Al Bayt University Karbala Iraq

ISBN: (纸本)9798350317022

Load balancing is the process of distributing the incoming traffic of the network among various resources and servers. When one of the servers fails, the load balancer transfers the network traffic to the other servers across the group. As soon as a server is joined to the group, the load balancer begins delivering traffic to that server as part of its balancing mechanism. It is a challenging effort to use distributed storage systems to manage a big amount of data and the synchronisation issue between many consumers or producers. In order to address this issue, this research proposal offers payload benchmarking with dynamic load balancing in distributed streaming storage systems using butterfly optimization algorithm (BOA). The main objective of the optimization technique is to analyze the benchmarking data and find the failure prediction of Kafka benchmarking. The BOA is used to solve optimization problems, particularly when there is insufficient or faulty data. To achieve the highest throughput from the streaming storage system, the performance of the benchmarking tool for distribution has been enhanced. The performance of the Kafka and DevOps systems is validated by the parameters of network latency as 3800ms, Execution time as 970ms, Memory size as 385kb, and Bandwidth as 380kb. The performance of the benchmarking tool for distributing the streaming storage system has been improved to achieve the maximum possible throughput from the streaming storage system. © 2023 IEEE.

关键词： butterfly optimization algorithm Cloud computing Distributed storage systems Kafka benchmark Load balancing

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A multi-objective butterfly optimization algorithm for protein encoding

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APPLIED SOFT COMPUTING 2023年 139卷

作者： Gonzalez-Sanchez, Belen Vega-Rodriguez, Miguel A. Santander-Jimenez, Sergio Univ Extremadura Escuela Politecn Campus Univ S-N Caceres 10003 Spain

The integration of multiple genes to maximize protein expression levels represents an important challenge in synthetic biology. This task relies on the definition of multiple protein-coding sequences, which must be as different as possible to avoid information loss. Proteins can be encoded in different ways, using synonymous codons that translate into the same amino acid. Some codons are better suited to the host than others, thus being preferable the use of the most fitting ones. However, adopting only the most highly adapted codons would lead to very similar coding sequences. An additional criterion is given by the fact that the designed sequences must contain a suitable guanine-cytosine (GC) ratio in accordance with the characteristics of the host organism. Therefore, this biological task requires the simultaneous optimization of several, conflicting objectives. This work proposes a novel multi-objective approach for protein encoding, which tackles the problem according to a new formulation based on three objective functions: codon adaptation index, Hamming distance between sequences, and GC content. Our work extends the recent butterfly optimization algorithm to multi-objective contexts, integrating problem-specific operators to boost solution quality by covering the different aspects required for accurate protein encoding. Two key structures, a taboo list and a best solution list, are defined to conduct improved searches attending to the potential improvements that each solution in the population can promote. Experiments conducted on nine real-world proteins reveal the attainment of relevant solutions from different evaluation perspectives, showing significant improvements over other single and multi-objective methods from the literature.& COPY;2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Multi-objective optimization butterfly optimization algorithm Protein encoding Bioinformatics Design of multiple genes

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Breast cancer prediction using a hybrid method based on butterfly optimization algorithm and Ant Lion Optimizer

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COMPUTERS IN BIOLOGY AND MEDICINE 2021年 139卷 104968-104968页

作者： Thawkar, Shankar Sharma, Satish Khanna, Munish Singh, Law Kumar Hindustan Coll Sci & Technol Dept Informat Technol Mathura Uttar Pradesh India RTM Nagpur Univ Dept Elect & Comp Sci Nagpur Maharashtra India Hindustan Coll Sci & Technol Dept Comp Sci & Engn Mathura Uttar Pradesh India

The design and development of a computer-based system for breast cancer detection are largely reliant on feature selection techniques. These techniques are used to reduce the dimensionality of the feature space by removing irrelevant or redundant features from the original set. This article presents a hybrid feature selection method that is based on the butterfly optimization algorithm (BOA) and the Ant Lion optimizer (ALO) to form a hybrid BOAALO method. The optimal subset of features chosen by BOAALO is utilized to predict the benign or malignant status of breast tissue using three classifiers: artificial neural network, adaptive neuro-fuzzy inference system, and support vector machine. The goodness of the proposed method is tested using 651 mammogram images. The results show that BOAALO outperforms the original BOA and ALO in terms of accuracy, sensitivity, specificity, kappa value, type-I, and type-II error as well as the receiver operating characteristics curve. Additionally, the suggested method's robustness is assessed and compared to five well-known methods using a benchmark dataset. The experimental findings demonstrate that BOAALO achieves a high degree of accuracy with a minimum number of features. These results support the suggested method's applicability for breast cancer diagnosis.

关键词： butterfly optimization algorithm Ant lion optimizer Artificial neural network Adaptive neuro-fuzzy inference system Support vector machine Feature selection Breast cancer Mammography

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Fuzzy fault tree analysis of EVAC system based on improved SAM-FFTA with butterfly optimization algorithm

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ENGINEERING FAILURE ANALYSIS 2023年 154卷

作者： Lu, Mingming Jin, Yajing Lin, Jieqiong Liu, Qiang Du, Yongsheng Yang, Yakun Changchun Univ Technol Sch Mechatron Engn Key Lab Micro Nano & Ultraprecis Mfg Jilin Prov Changchun 130012 Peoples R China Jilin Univ Sch Mechatron Engn Key Lab CNC Equipment Reliabil Minist Educ Changchun 130012 Peoples R China

At this stage, 3D elliptical vibration-assisted cutting(3D-EVAC) systems have proven to be a highly promising method of machining for a wide range of new materials, which can meet the requirements of ultra-precision machining. In practice, however, there are still various problems that affect its ability to achieve the desired accuracy. The functional reliability of the system should therefore be improved. Considering that the failure probability of 3D-EVAC system components cannot be obtained accurately, this paper proposes an improved similarity aggregation method (SAM) based on the butterfly optimization algorithm (BOA), called BOA-SAM. In the experiment, thirty-six events are selected and these events are aggregated by BOA-SAM. The experimental results show that BOA-SAM not only avoids the variability of the aggregation results with the relaxation factor (beta) but also improves the accuracy of the aggregation results compared with SAM. BOA-SAM and FTA are connected by the center-of-mass method to form BOA-SAM-FFTA. Finally, a fuzzy fault tree analysis of the 3D-EVAC system was carried out using the BOA-SAM-FFTA method to calculate the probability of failure for each bottom event and to analyze its importance. It was found that non-uniform heating of the tool was the event that had the greatest impact on the functional reliability of the system, which offered a theoretical basis for improving the functional reliability of the system. The probability of failure of other bottom events during the experiment also can provide some theoretical guidance for the system maintenance plan .

关键词： 3D-EVAC system butterfly optimization algorithm Similarity aggregation method Fuzzy fault tree analysis

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A modified butterfly optimization algorithm: An adaptive algorithm for global optimization and the support vector machine

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EXPERT SYSTEMS 2021年第3期38卷 e12642-e12642页

作者： Hu, Kun Jiang, Hao Ji, Chen-Guang Pan, Ze State Key Lab Min Response & Disaster Prevent & C Huainan 232001 Peoples R China Anhui Univ Sci & Technol Huainan Peoples R China

A modified adaptive butterfly optimization algorithm is established with the aim of addressing the "early search blindness" and the relatively poor adaptability of the sensory modality. A normal-distribution-based model and a Weibull-distribution-based adaptive model of sensory modalities are respectively proposed for the global search process and iteration process. Among them, the Weibull-distribution-based adaptive model of sensory modalities is mainly manifested as the c value, that is, the adaptive change trend based on the Weibull model. The performance of the modified butterfly optimization algorithm is validated using a 14-benchmark test function and compared with performances of some latest algorithms. The experimental results indicate that the modified algorithm performs competitively in terms of accuracy and stability. Following the experiment, the modified algorithm is further tested by running a support-vector-machine prediction model based on engineering data of a pipe belt conveyor's flat-pipe/pipe-flat transition segment. The results of the modified algorithm are then compared with test-run outcomes of the back-propagation prediction model and KCV-SVM model. The results show that the prediction error is well within 10%, demonstrating the method's competence as a reliable reference for future designs of pipe belt conveyors.

关键词： adaptive butterfly optimization algorithm support vector machine transition segment of the tubular belt conveyor

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Energy efficient cluster based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization

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AD HOC NETWORKS 2021年 110卷 102317-102317页

作者： Maheshwari, Prachi Sharma, Ajay K. Verma, Karan Natl Inst Technol Delhi India IKG Punjab Tech Univ Jalandhar Punjab India

Wireless Sensor Networks (WSNs) consist of a large number of spatially distributed sensor nodes connected through the wireless medium to monitor and record the physical information from the environment. The nodes of WSN are battery powered, so after a certain period it loose entire energy. This energy constraint affects the lifetime of the network. The objective of this study is to minimize the overall energy consumption and to maximize the network lifetime. At present, clustering and routing algorithms are widely used in WSNs to enhance the network lifetime. In this study, the butterfly optimization algorithm (BOA) is employed to choose an optimal cluster head from a group of nodes. The cluster head selection is optimized by the residual energy of the nodes, distance to the neighbors, distance to the base station, node degree and node centrality. The route between the cluster head and the base station is identified by using Ant Colony optimization (ACO), it selects the optimal route based on the distance, residual energy and node degree. The performance measures of this proposed methodology are analyzed in terms of alive nodes, dead nodes, energy consumption and data packets received by the BS. The outputs of the proposed methodology are compared with traditional approaches LEACH, DEEC and compared with some existing methods FUCHAR, CRHS, BERA, CPSO, ALOC and FLION. For example, the alive nodes of the proposed methodology are 200 at 1500 iterations which is higher compared to the CRHS and BERA methods.

关键词： Ant colony optimization butterfly optimization algorithm Energy consumption Network lifetime Wireless sensor networks

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Blind Source Separation Based on Double-Mutant butterfly optimization algorithm

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SENSORS 2022年第11期22卷 3979页

作者： Xia, Qingyu Ding, Yuanming Zhang, Ran Liu, Minti Zhang, Huiting Dong, Xiaoqi Dalian Univ Commun & Network Lab Dalian 116622 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

The conventional blind source separation independent component analysis method has the problem of low-separation performance. In addition, the basic butterfly optimization algorithm has the problem of insufficient search capability. In order to solve the above problems, an independent component analysis method based on the double-mutant butterfly optimization algorithm (DMBOA) is proposed in this paper. The proposed method employs the kurtosis of the signal as the objective function. By optimizing the objective function, blind source separation of the signals is realized. Based on the original butterfly optimization algorithm, DMBOA introduces dynamic transformation probability and population reconstruction mechanisms to coordinate global and local search, and when the optimization stagnates, the population is reconstructed to increase diversity and avoid falling into local optimization. The differential evolution operator is introduced to mutate at the global position update, and the sine cosine operator is introduced to mutate at the local position update, hence, enhancing the local search capability of the algorithm. To begin, 12 classical benchmark test problems were selected to evaluate the effectiveness of DMBOA. The results reveal that DMBOA outperformed the other benchmark algorithms. Following that, DMBOA was utilized for the blind source separation of mixed image and speech signals. The simulation results show that the DMBOA can realize the blind source separation of an observed signal successfully and achieve higher separation performance than the compared algorithms.

关键词： blind source separation independent component analysis butterfly optimization algorithm dynamic transformation probability population reconstruction mechanism differential evolution operator sine cosine operator

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